Computing device and method

ABSTRACT

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/614,215 filed on Nov. 15, 2019, which is a national stage applicationof PCT/CN2018/103850 filed Sep. 3, 2018 that claims the benefit ofpriority from Chinese Application No. 201810207915.8, filed Mar. 14,2018, and Chinese Application No. 201810149287.2, filed Feb. 13, 2018.The disclosures of the above-mentioned applications are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to the field of information processingtechnology, and specifically relates to a computing device and acomputing method.

BACKGROUND

With continuous development and growing demand of informationtechnology, there are increasing challenges in achieving informationtimeliness. Currently, the speed with which a terminal acquires andprocesses information is largely determined by how its general purposeprocessor operates.

In practice, it has been found that how a general purpose processor runsa software program to process information is limited by the processor'soperating speed. Generally, when there is a large load to process,general purpose processors are not efficient and are often the causesfor delay. For example, in machine learning, when training a learningmodel, the computation amount of the training operations is so largethat it will take an extraordinarily long time for general purposeprocessors to complete the computations needed to build up the learningmodel. The low efficiency of general purpose processors in trainingoperations makes them unsuitable for artificial intelligence relatedtasks.

SUMMARY

A computation device and a computation method are provided according toexamples of the present disclosure, which may improve the processingspeed of operations and the efficiency.

In a first aspect, there is provided a computation device. Thecomputation device may be configured to perform a machine learningcomputation, and may include a controller unit, an operation unit, and aconversion unit. The operation unit may include a primary processingcircuit and a plurality of secondary processing circuits.

The controller unit may be configured to acquire first input data and acomputation instruction, to parse the computation instruction to obtainat least one of a data conversion instruction and at least one operationinstruction, where the data conversion instruction may include an opcodefield and an opcode. The opcode may be configured to indicateinformation of a function of the data conversion instruction. The opcodefield may include information of a decimal point position, a flag bitindicating a data type of the first input data, and an identifier ofdata type conversion. The controller unit may be further configured totransfer the opcode and the opcode field of the data conversioninstruction and the first input data to the conversion unit, and to senda plurality of operation instructions to the operation unit.

The conversion unit may be configured to convert the first input datainto second input data according to the opcode and the opcode field ofthe data conversion instruction, where the second input data isfixed-point data, and to transfer the second input data to the operationunit.

The operation unit may be configured to perform operations on the secondinput data according to the plurality of operation instructions toobtain a computation result of the computation instruction.

In an example, the machine learning computation may include anartificial neural network operation. The first input data may include aninput neuron and a weight. The computation result is an output neuron.

In an example, the operation unit may include the primary processingcircuit and the plurality of secondary processing circuits.

The primary processing circuit may be configured to performpre-processing on the second input data and to send data and theplurality of operation instructions between the plurality of secondaryprocessing circuits and the primary processing circuit.

The plurality of secondary processing circuits may be configured toperform an intermediate operation to obtain a plurality of intermediateresults according to the second input data and the plurality ofoperation instructions sent from the primary processing circuit, and totransfer the plurality of intermediate results to the primary processingcircuit.

The primary processing circuit may be further configured to performpost-processing on the plurality of intermediate results to obtain thecomputation result of the computation instruction.

In an example, the computation device may further include a storage unitand a direct memory access (DMA) unit;

the storage unit may include any combination of a register and a cache;

the cache may include a scratch pad cache and may be configured to storethe first input data; and

the register may be configured to store scalar data in the first inputdata.

In a second aspect, there is provided a method for performing machinelearning computations. The method may include the following:

obtaining input data and a computation instruction;

parsing the computation instruction to obtain a data conversioninstruction and a plurality of operation instructions, in which the dataconversion instruction may include an opcode field and an opcode, wherethe opcode may be configured to indicate information of a function ofthe data conversion instruction, and the opcode field may includeinformation of a decimal point position, a flag bit indicating a datatype of the first input data, and a data type conversion;

converting the first input data into second input data according to thedata conversion instruction, and the second input data is fixed-pointdata; and

performing operations on the second input data according to theplurality of operation instructions to obtain a computation result ofthe computation instruction.

In a third aspect, a machine learning operation device is provided. Themachine learning operation device may include at least one computationdevice of the first aspect. The at least one computation device may beconfigured to obtain data to be processed and control information fromother processing devices, to perform specified machine learningcomputations, and to send an execution result to the other processingdevices through I/O interfaces.

If the machine learning operation device includes a plurality of thecomputation devices, the plurality of computation devices may beconfigured to couple and exchange data with each other through aspecific structure.

The plurality of computation devices may be configured to interconnectand to transfer data through a PCIE (peripheral component interfaceexpress) bus to support larger-scale machine learning computations, toshare the same one control system or have respective control systems, toshare the same one memory or have respective memories, and to deploy aninterconnection manner of any arbitrary interconnection topology.

In a fourth aspect, a combination processing device is provided. Thecombination processing device may include the machine learning operationdevice of the third aspect, universal interconnection interfaces, andother processing devices. The machine learning operation device may beconfigured to interact with the other processing devices to jointlyperform user-specified computing operations. The combination processingdevice may further include a storage device. The storage device may beconfigured to couple with the machine learning operation device and theother processing devices for storing data of the machine learningoperation device and the other processing devices

In a fifth aspect, a neural network chip is provided. The neural networkchip may include one of the machine learning operation device of thefirst aspect, the machine learning operation device of the third aspect,and the combination processing device of the fourth aspect.

In a sixth aspect, a neural network chip package structure is provided.The neural network chip package structure may include the neural networkchip of the fifth aspect.

In a seventh aspect, a board is provided. The board may include astorage device, an interface device, a control device, and the neuralnetwork chip of the fifth aspect.

The neural network chip is respectively coupled with the storage device,the control device, and the interface device.

The storage device may be configured to store data.

The interface device may be configured to implement data transmissionbetween the neural network chip and external devices.

The control device may be configured to monitor a status of the neuralnetwork chip.

Furthermore, the storage device may include a plurality of groups ofstorage units, each group of the plurality of groups of the storageunits is coupled with the neural network chip through a bus, and thestorage unit being a double data rate (DDR) synchronous dynamic randomaccess memory (SDRAM).

The neural network chip may include a DDR controller for controllingdata transmission and data storage of each of the storage units.

The interface device may include standard PCIE interfaces.

In an eighth aspect, an electronic device is provided. The electronicdevice may include one of the neural network chip of the fifth aspect,the neural network chip package structure of the sixth aspect, and theboard of the seventh aspect.

In examples of the present disclosure, the electronic device may includedata processing devices, robots, computers, printers, scanners, tablets,smart terminals, mobile phones, driving recorders, navigators, sensors,cameras, servers, cloud servers, cameras, cameras, projectors, watches,headphones, mobile storage, wearable devices, vehicles, householdappliances, and/or medical devices.

The vehicle may include an aircraft, a ship, and/or a car. The householdappliance may include a television, an air conditioner, a microwaveoven, a refrigerator, a rice cooker, a humidifier, a washing machine, anelectric lamp, a gas stove, a range hood. The medical device may includea nuclear magnetic resonance instrument, a B-ultrasound, and/or anelectrocardiograph.

In the examples of the present disclosure, the computation device mayinclude an operation unit, a controller unit, and a conversion unit. Thecontroller unit may be configured to extract a computation instructionfrom a storage unit, and to parse the computation instruction to obtainat least one of a data conversion instruction and at least one operationinstruction, and to send the data conversion instruction, at least oneoperation instruction, and first input data to the operation unit. Theoperation unit may be configured to convert the first input data intothe second input data represented by fixed-point data according to thedata conversion instruction, and to perform operations on the secondinput data according to the at least one operation instruction to obtaina computation result of the computation instruction. In the examples ofthe present disclosure, the first data involved in machine learningcomputations is represented by fixed-point data, thereby improving theprocessing speed and processing efficiency of training operations.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the examples of the presentdisclosure more clearly, the following briefly introduces theaccompanying drawings required for describing the examples. Apparently,the accompanying drawings in the following description illustrate someexamples of the present disclosure. Those of ordinary skill in the artmay also obtain other drawings based on these accompanying drawingswithout creative efforts.

FIG. 1 is a schematic diagram of a data structure of fixed-point dataaccording to an example of the present disclosure;

FIG. 2 is a schematic diagram of another data structure of fixed-pointdata according to an example of the present disclosure;

FIG. 2A is a schematic diagram of yet another data structure offixed-point data according to an example of the present disclosure;

FIG. 2B is a schematic diagram of a still another data structure offixed-point data according to an example of the present disclosure;

FIG. 3 is a schematic structural diagram of a computation deviceaccording to an example of the present disclosure;

FIG. 3A is a schematic structural diagram of another computation deviceaccording to an example of the present disclosure;

FIG. 3B is a schematic structural diagram of yet another computationdevice according to an example of the present disclosure;

FIG. 3C is a schematic structural diagram of still another computationdevice according to an example of the present disclosure;

FIG. 3D is a schematic structural diagram of a primary processingcircuit according to an example of the present disclosure;

FIG. 3E is a schematic structural diagram of still another computationdevice according to an example of the present disclosure;

FIG. 3F is a schematic structural diagram of a tree module according toan example of the present disclosure;

FIG. 3G is a schematic structural diagram of still another computationdevice according to an example of the present disclosure;

FIG. 3H is a schematic structural diagram of still another computationdevice according to an example of the present disclosure;

FIG. 4 is a flow chart of a forward operation of a single-layerartificial neural network according to an example of the presentdisclosure;

FIG. 5 is a flow chart of a forward and reverse training of a neuralnetwork according to an example of the present disclosure;

FIG. 6 is a structural diagram of a combined processing device accordingto an example of the present disclosure;

FIG. 6A is a schematic structural diagram of still yet anothercomputation device according to an example of the present disclosure;

FIG. 7 is a structural diagram of another combined processing deviceaccording to an example of the present disclosure;

FIG. 8 is a schematic structural diagram of a board according to anexample of the present disclosure;

FIG. 9 is a schematic flow chart of a computation method according to anexample of the present disclosure;

FIG. 10 is a schematic flow chart of a process for determining andadjusting a decimal point position of data according to an example ofthe present disclosure;

FIG. 11 is a schematic structural diagram of a distributed systemaccording to an example of the present disclosure;

FIG. 12 is a schematic structural diagram of another distributed systemaccording to an example of the present disclosure.

DETAILED DESCRIPTION OF ILLUSTRATED EXAMPLES

Technical solutions in the examples of the present disclosure will bedescribed clearly and completely hereinafter with reference to theaccompanying drawings in the examples of the present disclosure.Apparently, the described examples are merely some rather than allexamples of the present disclosure. All other examples obtained by thoseof ordinary skill in the art based on the examples of the presentdisclosure without creative efforts shall fall within the protectionscope of the present disclosure.

The terms such as “first”, “second”, “third”, and “fourth” used in thespecification, the claims, and the accompanying drawings of the presentdisclosure are used for distinguishing between different objects ratherthan describing a particular order. The terms “include” and “comprise”as well as variations thereof are intended to cover non-exclusiveinclusion. For example, a process, method, system, product, device, orapparatus including a series of steps or units is not limited to thelisted steps or units, it may optionally include other steps or unitsthat are not listed; alternatively, other steps or units inherent to theprocess, method, product, or device may be included either.

The term “example” or “implementation” referred to herein means that aparticular feature, structure, or feature described in conjunction withthe example may be contained in at least one example of the presentdisclosure. The phrase appearing in various places in the specificationdoes not necessarily refer to the same example, nor does it refer to anindependent or alternative example that is mutually exclusive with otherexamples. It is expressly and implicitly understood by those skilled inthe art that an example described herein may be combined with otherexamples.

Examples of the present disclosure provide a data type. The data typemay include an adjustment factor. The adjustment factor may beconfigured to indicate a value range and precision of the data type.

The adjustment factor may include a first scaling factor. Optionally,the adjustment factor may further include a second scaling factor. Thefirst scaling factor may be configured to indicate the precision of thedata type, and the second scaling factor may be configured to adjust thevalue range of the data type.

Optionally, the first scaling factor may be 2^(−m), 8^(−m), 10^(−m), 2,3, 6, 9, 10, 2^(m), 8^(m), 10^(m), or other values.

In an example, the first scaling factor may be configured to adjust adecimal point position. For example, an input data INB1=INA1*2m may beobtained by shifting the decimal point position of an input data INA1expressed in binary to the right by m bits, that is, the input data INB1is 2^(m) times greater than the input data INA1. For another example, aninput data INB2=INA2/10^(n) may be obtained by shifting the decimalpoint position of an input data INA2 expressed in decimal to the left byn bits, that is, the input data INB2 is 10^(n) times less than the inputdata INA2, and m and n are integers.

Optionally, the second scaling factor may be 2, 8, 10, 16, or othervalues.

For example, the value range of the data type corresponding to the inputdata is 8⁻¹⁵-8¹⁶ In the process of performing operations, if anoperation result obtained is greater than the maximum valuecorresponding to the value range of the data type corresponding to theinput data, the value range of the data type is multiplied by a secondscaling factor (for example, 8) of the data type to obtain a new valuerange of 8⁻¹⁴-8¹⁷. If the operation result obtained is smaller than theminimum value corresponding to the value range of the data typecorresponding to the input data, divide the value range of the data typeby the second scaling factor (for example, 8) of the data type to obtaina new value range of 8⁻¹⁶-8¹⁵.

Scaling factors may be applied to any format of data (such as floatingpoint data and discrete data), so as to adjust the size and precision ofthe data.

It should be noted that the decimal point position mentioned in thespecification of the present disclosure may be adjusted by theabove-identified first scaling factor, which will not be describedherein.

The structure of fixed-point data is described as follows. FIG. 1 is aschematic diagram of a data structure of the fixed-point data accordingto an example of the present disclosure. As illustrated in FIG. 1,signed fixed-point data is provided. The signed fixed-point dataoccupies X bits, which may be referred to as X-bit fixed-point data. TheX-bit fixed-point data may include a 1-bit sign bit, an M-bit integerbit, and an N-bit decimal bit, where X−1=M+N. For unsigned fixed-pointdata, only an integer bit of M bits and a decimal bit of N bits areincluded, that is, X=M+N.

Compared with a 32-bit floating point data representation, the short-bitfixed-point data representation adopted by the present disclosureoccupies fewer bits. In addition, for data of the same layer of the sametype in a network model (such as all convolution kernels, input neurons,or offset data of the first convolutional layer), the short-bitfixed-point data representation adopted by the present disclosure may befurther provided with a flag bit for recording the decimal pointposition of the fixed-point data, where the flag bit represents a pointlocation. In this way, the value of the above-mentioned flag bit may beadjusted according to the distribution of the input data, therebyachieving the adjustment of the precision and the representable range ofthe fixed-point data.

For example, a floating point number 68.6875 may be converted to signed16-bit fixed-point data with a decimal point position of five, where theinteger part of the floating point number occupies 10 bits, thefractional part of the floating point number occupies 5 bits, and thesign bit of the floating point number occupies 1 bit. As illustrated inFIG. 2, a conversion unit may be configured to convert theabove-mentioned floating point number 68.6875 into signed 16-bitfixed-point data of 0000010010010110.

In an example, the above-mentioned fixed-point data may also berepresented in the manner illustrated in FIG. 2A. As illustrated in FIG.2A, “bitnum” represents the bit number occupied by the fixed-point data,“s” represents the decimal point position, and “2s” represents theprecision of the fixed-point data. The first bit is a sign bit thatindicates the data is positive or negative. For example, if the sign bitis zero, it indicates that the fixed-point data is a positive number; ifthe sign bit is one, it indicates that the fixed-point data is anegative number. The value of the fixed-point data ranges from “neg” to“pos”, where pos=(2^(bitnum−1)−1)*2^(s), andneg=−(2^(bitnum−1)−1)*2^(s), wherein the above-mentioned bitnum may beany positive integer. The above-mentioned s may be any integer not lessthan s_min).

In an example, the above-mentioned bitnum may be 8, 16, 24, 32, 64, orother values. Further, the above-mentioned s_min is −64.

In an example, the above-mentioned bitnum may be 8, 16, 24, 32, 64, orother values. The above-mentioned s may be any integer not less thans_min. Further, the above-mentioned s_min is −64.

In an example, a plurality of fixed-point representations may be adoptedfor data having a larger value, as illustrated in FIG. 2B. Referring toFIG. 2B, the data having a larger value is represented by a combinationof three kinds of fixed-point data, that is, the fixed-point data mayinclude fixed-point data 1, fixed-point data 2, and fixed-point data 3.The bit width of the fixed-point data 1 is bitnum1 the decimal pointposition of the fixed-point data 1 is s1; the bit width of thefixed-point data 2 is bitnum2, and the decimal point position of thefixed-point data 2 is s2; the bit width of the fixed-point data 3 isbitnum3, and the decimal point position of the fixed-point data 3 is s3,where bitnum2−2=s1−1, bitnum3−2=s2−1. The range represented by the threefixed-point data is [neg, pos], where pos=(2^(bitnum−1)−1)*2^(s),neg=−(2^(bitnum−1)−1)*2^(s).

First, a computation device adopted in the present disclosure will beintroduced. Referring to FIG. 3, the computation device is provided. Thecomputation device may include a controller unit 11, an operation unit12, and a conversion unit 13, where the controller unit 11 is coupledwith the operation unit 12, and the conversion unit 13 is coupled withthe controller unit 11 and the operation unit 12.

In an example, a controller unit 11 may be configured to acquire firstinput data and a computation instruction.

In an example, the first input data may be machine learning data.Further, the machine learning data may include input neurons andweights. Output neurons are final output data or intermediate data.

In an example, the first input data and the computation instruction maybe acquired via a data input and output unit. The data input and outputunit may specifically include one or more data I/O interfaces or I/Opins.

The above-mentioned computation instruction may include, but is notlimited to, a forward operation instruction, a reverse traininginstruction, other neural network operation instructions (such as aconvolution operation instruction), and the like. The examples of thepresent disclosure do not limit the specific expression of theabove-mentioned computation instruction.

The controller unit 11 may be further configured to parse thecomputation instruction to obtain at least one of a data conversioninstruction and at least one operation instruction, where the dataconversion instruction may include an opcode field and an opcode. Theopcode may be configured to indicate information of a function of thedata conversion instruction. The opcode field may include information ofa decimal point position, a flag bit indicating a data type of the firstinput data, and an identifier of data type conversion.

If the opcode field of the data conversion instruction is correspondingto an address of a storage space, the controller unit 11 may beconfigured to obtain the decimal point position, the flag bit indicatingthe data type of the first input data, and the identifier of the datatype conversion according to the storage space corresponding to theaddress.

The controller unit 11 may be configured to transfer the opcode and theopcode field of the data conversion instruction and the first input datato a conversion unit 13, and to send multiple operation instructions toan operation unit 12.

The conversion unit 13 may be configured to convert the first input datainto second input data according to the opcode and the opcode field ofthe data conversion instruction, where the second input data isfixed-point data, and to transfer the second input data to the operationunit 12.

The operation unit 12 may be configured to perform operations on thesecond input data according to the multiple operation instructions toobtain a computation result of the computation instruction.

In an example, according to the technical solution provided by thepresent disclosure, the operation unit 12 may be configured as aone-master multi-slave structure. For the forward operation instruction,data may be divided according to the forward operation instruction. Inthis way, multiple secondary processing circuits 102 may performparallel operations on data with a large amount of computation, therebyincreasing the operation speed, saving computation time, and furtherreducing power consumption. As illustrated in FIG. 3A, the operationunit 12 may include a primary processing circuit 101 and the multiplesecondary processing circuits 102.

The primary processing circuit 101 may be configured to performpre-processing on the second input data and to send data and themultiple operation instructions between the multiple secondaryprocessing circuits 102 and the primary processing circuit 101.

The multiple secondary processing circuits 102 may be configured toperform intermediate operations to obtain multiple intermediate resultsaccording to the second input data and the multiple operationinstructions sent from the primary processing circuit 101, and totransfer the multiple intermediate results to the primary processingcircuit 101.

The primary processing circuit 101 may be further configured to performpost-processing on the multiple intermediate results to obtain thecomputation result of the computation instruction.

In an example, the machine learning computations may include deeplearning operations (that is, artificial neural network operations).Machine learning data (that is, the first input data) may include inputneurons and weights. Output neurons include the computation result orthe multiple intermediate results of the computation instruction. Thedeep learning operations are described as an example, but it should beunderstood that it is not limited to the deep learning operations.

In an example, the computation device may further include a storage unit10 and a direct memory access (DMA) unit 50, where the storage unit 10may include one or any combination of a register and a cache.Specifically, the cache may be configured to store the computationinstruction. The register 201 may be configured to store the first inputdata and scalar data. The first input data may include the inputneurons, the weights, and the output neurons.

The cache 202 may be a scratch pad cache.

The DMA unit 50 may be configured to read or store data from the storageunit 10.

In an example, the above-mentioned register 201 may be configured tostore the multiple operation instructions, the first input data, thedecimal point position, the flag bit indicating the data type of thefirst input data, and the identifier of data type conversion. Thecontroller unit 11 may be configured to directly acquire the multipleoperation instructions, the first input data, the decimal pointposition, the flag bit indicating the data type of the first input data,and the identifier of the data type conversion from the register 201.The controller unit 11 may be configured to transfer the first inputdata, the decimal point position, the flag bit indicating the data typeof the first input data, and the identifier of the data type conversionto the conversion unit 13, and to send the multiple operationinstructions to the operation unit 12.

The conversion unit 13 may be configured to convert the first input datainto the second input data according to the decimal point position, theflag bit indicating the data type of the first input data, and theidentifier of the data type conversion, and to transfer the second inputdata to the operation unit 12.

The operation unit 12 may be configured to perform operations on thesecond input data according to the multiple operation instructions toobtain a computation result.

In an example, the controller unit 11 may include an instruction cacheunit 110, an instruction processing unit 111, and a storage queue unit113.

The instruction cache unit 110 may be configured to store thecomputation instruction associated with artificial neural networkoperations.

The instruction processing unit 111 may be configured to parse thecomputation instruction to obtain the data conversion instruction andthe multiple operation instructions, and to parse the data conversioninstruction to obtain the opcode and the opcode field of the dataconversion instruction.

The storage queue unit 113 may be configured to store an instructionqueue, the instruction queue may include the multiple operationinstructions or the computation instruction to be executed in asequence.

For example, in an optional technical solution, the primary processingcircuit 101 may also include a control unit, which may include a primaryinstruction processing unit for decoding instructions intomicroinstructions. Of course, in another example, the secondaryprocessing circuit 102 may also include another control unit, which mayinclude a secondary instruction processing unit for receiving andprocessing the microinstructions. The microinstructions may be at a nextstage of the instructions, and the microinstruction may be obtained bysplitting or decoding the instructions, and may be further decoded intocontrol signals of each component, each unit or each processing circuit.

In an example, the structure of the computation instruction may be shownas in Table 1 below.

TABLE 1 Opcode Register or Immediate Register/Immediate data . . . . . .

The ellipsis in the above table indicates that multiple registers orimmediate data may be included.

In another example of the present disclosure, the computationinstruction may include at least one opcode field and one opcode. Thecomputation instruction may include neural network operationinstructions. The neural network operation instructions are described asan example, as illustrated in Table 2, where the register number 0, theregister number 1, the register number 2, the register number 3, and theregister number 4 may be opcode fields. Each of the register number 0,the register number 1, the register number 2, the register number 3, andregister number 4 may correspond to one or more registers.

TABLE 2 Register Register Register Register Register Opcode number 0number 1 number 2 number 3 number 4 COMPUTE Starting Length of StartingLength of Address of address of the input address of the weightactivation the input data the weight function data inter- polation tableIO Address of Data Address of external length internal data data memorymemory NOP JUMP Desti- nation address MOVE Input Data size Outputaddress address

The above-mentioned registers may be off-chip memories. Of course, inpractice, the above-mentioned registers may also be on-chip memories forstoring data, and the data may be n-dimensional data, where n is aninteger greater than or equal to one. For example, if n=1, the data is aone-dimensional data (that is, a vector). For another example, if n=2,the data is a two-dimensional data (that is, a matrix). If n=3 or more,the data is a multi-dimensional tensor.

In an example, the controller unit 11 may further include a dependencyrelationship processing unit 112. If the multiple operation instructionsare provided, the dependency relationship processing unit 112 may beconfigured to determine whether there exists an associated relationshipbetween a first operation instruction and a zeroth operation instructionbefore the first operation instruction, to cache the first operationinstruction in the instruction cache unit 110 based on a determinationthat there exists an associated relationship between the first operationinstruction and the zeroth operation instruction, and to extract thefirst operation instruction from the instruction cache unit 110 to theoperation unit, if an execution of the zeroth operation instruction iscompleted.

The dependency relationship processing unit 112 configured to determinewhether there exists an associated relationship between a firstoperation instruction and a zeroth operation instruction before thefirst operation instruction may be configured to extract a first storageaddress interval of data required (such as a matrix) in the firstoperation instruction according to the first operation instruction, toextract a zeroth storage address interval of matrix required in thezeroth operation instruction according to the zeroth operationinstruction, to determine that there exists an associated relationshipbetween the first operation instruction and the zeroth operationinstruction, if an overlapped region exists between the first storageaddress interval and the zeroth storage address interval, and todetermine that there does not exist an associated relationship betweenthe first operation instruction and the zeroth operation instruction, ifno overlapped region exists between the first storage address intervaland the zeroth storage address interval.

In an example, as illustrated in FIG. 3B, the operation unit 12 mayinclude a primary processing circuit 101, multiple secondary processingcircuits 102, and multiple branch processing circuits 103.

The primary processing circuit 101 may be configured to determine thatthe input neurons are broadcast data and the weights are distributiondata, to divide the distribution data into multiple data blocks, and tosend at least one of the multiple data blocks, the broadcast data, andat least one of the multiple operation instructions to the branchprocessing circuits 103.

The multiple branch processing circuits 103 may be configured to forwardthe data blocks, the broadcast data, and the multiple operationinstructions transferred among the primary processing circuit 101 andthe multiple secondary processing circuits 102.

The multiple secondary processing circuits 102 may be configured toperform operations on the data blocks received and the broadcast datareceived according to the multiple operation instructions to obtainmultiple intermediate results, and to transfer the multiple intermediateresults to the plurality of branch processing circuits 103.

The primary processing circuit 101 may be further configured to performpost-processing on the multiple intermediate results received from thebranch processing circuits 103 to obtain a computation result of thecomputation instruction, and to send the computation result of thecomputation instruction to the controller unit 11.

In another example of the present disclosure, as illustrated in FIG. 3C,the operation unit 12 may include a primary processing circuit 101 andmultiple secondary processing circuits 102. The multiple secondaryprocessing circuits 102 are distributed in an array. Each secondaryprocessing circuit 102 is coupled with adjacent other secondaryprocessing circuits 102. The primary processing circuit 101 is coupledwith K secondary processing circuits 102 of the plurality of secondaryprocessing circuits 102. The K secondary processing circuits 102 mayinclude n secondary processing circuits 102 in the first row, nsecondary processing circuits 102 in the m^(th) row, and m secondaryprocessing circuits 102 in the first column. It should be noted that theK secondary processing circuits 102 as illustrated in FIG. 3C includeonly the n secondary processing circuits 102 in the first row, the nsecondary processing circuits 102 in the m^(th) row, and the m secondaryprocessing circuits 102 in the first column. That is, the K secondaryprocessing circuits 102 of the multiple secondary processing circuits102 are directly coupled with the primary processing circuit 101.

The K secondary processing circuits 102 may be configured to forwarddata and instructions transferred among the primary processing circuit101 and the multiple secondary processing circuits 102.

The primary processing circuit 101 may be further configured todetermine that the input neurons are broadcast data, the weights aredistribution data, to divide the distribution data into multiple datablocks, and to send at least one of the multiple data blocks and atleast one of the multiple operation instructions to the K secondaryprocessing circuits 102.

The K secondary processing circuits 102 may be configured to convert thedata transferred among the primary processing circuit 101 and theplurality of secondary processing circuits 102.

The multiple secondary processing circuits 102 may be configured toperform operations on the data blocks received according to the multipleoperation instructions to obtain multiple intermediate results, and totransfer the multiple intermediate results to the K secondary processingcircuits 102.

The primary processing circuit 101 may be configured to process themultiple intermediate results received from the K secondary processingcircuits 102 to obtain the computation result of the computationinstruction, and to send the computation result of the computationinstruction to the controller unit 11.

In an example, as illustrated in FIG. 3D, the primary processing circuit101 illustrated in FIGS. 3A-3C may further include one or anycombination of an activation processing circuit 1011 and an additionprocessing circuit 1012.

The activation processing circuit 1011 may be configured to perform anactivation operation on data in the primary processing circuit 101.

The addition processing circuit 1012 may be configured to perform anaddition operation or an accumulation operation.

The multiple secondary processing circuits 102 include multiplicationprocessing circuits configured to perform multiplication operations onthe data block received to obtain product results. The multiplesecondary processing circuits 102 may further include a forwardingprocessing circuit configured to perform forward processing on the datablock received or the product results. The multiple secondary processingcircuits 102 may further include accumulation processing circuitsconfigured to perform accumulation operations on the product results toobtain intermediate results.

In an example, the data type of the first input data is inconsistentwith an operation type indicated by the multiple operation instructionsinvolved in the operations, and the data type of the second input datais inconsistent with the operation type indicated by the multipleoperation instructions involved in the operations. The conversion unit13 may be configured to obtain the opcode and the opcode field of thedata conversion instruction. The opcode may be configured to indicateinformation of a function of the data conversion instruction. The opcodefield may include information of the decimal point position, the flagbit indicating the data type of the first input data, and the identifierof data type conversion. The conversion unit 13 may be configured toconvert the first input data into the second input data according to thedecimal point position and the identifier of data type conversion.

Specifically, identifiers of data type conversion are in one-to-onecorrespondence with conversion manners of data type. Table 3 is a tableillustrating correspondence relation between the identifier of data typeconversion and the conversion manner of data type.

As illustrated in Table 3, if the identifier of data type conversion is00, the conversion manner of data type is converting the fixed-pointdata into fixed-point data. If the identifier of data type conversion is01, the conversion manner of data type is converting the floating pointdata into floating point data. If the identifier of data type conversionis 10, the conversion manner of data type is converting the fixed-pointdata into floating point data. If the identifier of data type conversionis 11, the conversion manner of data type is converting the floatingpoint data into fixed-point data.

TABLE 3 Identifier of data type conversion Conversion manner of datatype 00 Converting the fixed-point data into fixed-point data 01Converting the floating point data into floating point data 10Converting the fixed-point data into floating point data 11 Convertingthe floating point data into fixed-point data

Optionally, the correspondence relationship between the identifier ofdata type conversion and the conversion manner of data type may also beas illustrated in Table 4.

TABLE 4 Iden- tifier of data type con- version Conversion manner of datatype 0000 Converting 64-bit fixed-point data into 64-bit floating pointdata 0001 Converting 32-bit fixed-point data into 64-bit floating pointdata 0010 Converting 16-bit fixed-point data into 64-bit floating pointdata 0011 Converting 32-bit fixed-point data into 32-bit floating pointdata 0100 Converting 16-bit fixed-point data into 32-bit floating pointdata 0101 Converting 16-bit fixed-point data into 16-bit floating pointdata 0110 Converting 64-bit floating point data into 64-bit fixed-pointdata 0111 Converting 32-bit floating point data into 64-bit fixed-pointdata 1000 Converting 16-bit floating point data into 64-bit fixed-pointdata 1001 Converting 32-bit floating point data into 32-bit fixed-pointdata 1010 Converting 16-bit floating point data into 32-bit fixed-pointdata 1011 Converting 16-bit floating point data into 16-bit fixed-pointdata

As illustrated in Table 4, if the identifier of data type conversion is0000, the conversion manner of data type is converting 64-bitfixed-point data into 64-bit floating point data. If the identifier ofdata type conversion is 0001, the conversion manner of data type isconverting 32-bit fixed-point data into 64-bit floating point data. Ifthe identifier of data type conversion is 0010, the conversion manner ofdata type is converting 16-bit fixed-point data into 64-bit floatingpoint data. If the identifier of data type conversion is 0011, theconversion manner of data type is converting 32-bit fixed-point datainto 32-bit floating point data. If the identifier of data typeconversion is 0100, the conversion manner of data type is converting16-bit fixed-point data into 32-bit floating point data. If theidentifier of data type conversion is 0101, the conversion manner ofdata type is converting 16-bit fixed-point data into 16-bit floatingpoint data. If the identifier of data type conversion is 0110, theconversion manner of data type is converting 64-bit floating point datainto 64-bit fixed-point data. If the identifier of data type conversionis 0111, the conversion manner of data type is converting 32-bitfloating point data into 64-bit fixed-point data. If the identifier ofdata type conversion is 1000, the conversion manner of data type isconverting 16-bit floating point data into 64-bit fixed-point data. Ifthe identifier of data type conversion is 1001, the conversion manner ofdata type is converting 32-bit floating point data into 32-bitfixed-point data. If the identifier of data type conversion is 1010, theconversion manner of data type is converting 16-bit floating point datainto 32-bit fixed-point data. If the identifier of data type conversionis 1011, the conversion manner of data type is converting 16-bitfloating point data into 16-bit fixed-point data.

In an example, the controller unit 11 may be configured to acquire acomputation instruction from the storage unit 10, and to parse thecomputation instruction to obtain at least one operation instruction,where the operation instruction may be a variable format operationinstruction or a fixed-point format operation instruction.

The variable format operation instruction may include an opcode and anopcode field. The opcode may be configured to indicate a function of thevariable format operation instruction. The opcode field may include afirst address of first input data, the length of the first input data(optionally), a first address of output data, a decimal point position,a flag bit indicating a data type of the first input data (optionally),and an operation type identifier.

If the above-mentioned operation instruction is the variable formatoperation instruction, the above-mentioned controller unit 11 parses theabove-mentioned variable format operation instruction, so as to obtainthe first address of the first input data, the length of the first inputdata, the first address of the output data, the decimal point position,the flag bit indicating the data type of the first input data, and theoperation type identifier. Further, the controller unit 11 obtains thefirst input data from the storage unit 10 according to the first addressof the first input data and the length of the first input data. Andthen, the controller unit 11 transfers the first input data, the decimalpoint position, the flag bit indicating the data type of the first inputdata, and the operation type identifier to the conversion unit 13, andsends the first address of the output data to the operation unit 12.

The conversion unit 13 may be configured to convert the first input datainto the second input data according to the flag bit indicating the datatype, the decimal point position, and an operation type indicated by theoperation type identifier, and to transfer the second input data to theoperation unit 12.

The primary processing circuit 101 and the secondary processing circuits102 of the operation unit 12 may be configured to perform operations onthe second input data, so as to obtain the computation result of thecomputation instruction, and to store the computation result of thecomputation instruction in a position corresponding to the first addressof the output data in the storage unit 10.

The operation type identifier may be configured to indicate a type ofdata involved in operations if the operation unit 12 performs theoperations. The type may include fixed-point data, floating point data,integer data, discrete data, or the like.

In an example, the storage unit 10 may be configured to store the firstaddress of the first input data, the length of the first input data, thefirst address of the output data, the decimal point position, the flagbit indicating the data type of the first input data, and the operationtype identifier. The controller unit 11 may be configured to directlyacquire the first address of the first input data, the length of thefirst input data, the first address of the output data, the decimalpoint position, the flag bit indicating the data type of the first inputdata, and the operation type identifier from the storage unit 10. Andthen, subsequent operations may be performed as the above process.

For example, the above-mentioned operation type identifier may be zeroor one. If the flag bit is one, the primary processing circuit 101 andthe secondary processing circuits 102 of the operation unit 12 performfloating point operations, that is, the data type of data involved inthe floating point operations is floating point type. If the flag bit iszero, the primary processing circuit 101 and the secondary processingcircuits 102 of the operation unit 12 perform fixed-point operations,that is, the data type of data involved in the fixed-point operations isfixed-point type.

The operation unit 12 may determine the data type of the input data andthe operation type according to the flag bit and the operation typeidentifier.

Specifically, referring to Table 5. Table 5 is a mapping relationshiptable of the flag bits and the operation type identifiers.

TABLE 5 Operation type Flag bit indicating data type identifier 0 1 0The first input data is fixed- The first input data is point data, thefixed-point floating point data, the operations are performed floatingpoint operations are performed 1 The first input data is fixed- Thefirst input data is point data, converting the floating point data,fixed-point data into floating converting the floating point data, thefloating point point data into fixed-point operations are performeddata, the fixed-point operations are performed

As illustrated in Table 5, if the operation type identifier is 0 and theflag bit indicating the data type is 0, the first input data isfixed-point data, and the primary processing circuit 101 and thesecondary processing circuits 102 of the operation unit 12 performfixed-point operations without performing data type conversion. If theoperation type identifier is 0 and the flag bit indicating the data typeis 1, the first input data is floating point data, and the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform floating point operations without performingdata type conversion. If the operation type identifier is 1 and the flagbit indicating the data type is 0, the first input data is fixed-pointdata, and the conversion unit 13 first converts the first input datainto the second input data according to the decimal point position,where the second input data is floating point data, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform operations on the second input data. If theoperation type identifier is 1 and the flag bit indicating the data typeis 1, the first input data is floating point data, and the conversionunit 13 first converts the first input data into the second input dataaccording to the decimal point position, where the second input data isfixed-point data, the primary processing circuit 101 and the secondaryprocessing circuits 102 of the operation unit 12 perform operations onthe second input data.

The above-mentioned fixed-point data may include 64-bit fixed-pointdata, 32-bit fixed-point data, and 16-bit fixed-point data. Theabove-mentioned floating point data may include 64-bit floating pointdata, 32-bit floating point data, and 16-bit floating point data. Themapping relationship between the above-mentioned flag bits and theoperation type identifiers is illustrated in Table 6.

As illustrated in Table 6, if the operation type identifier is 0000 andthe flag bit indicating the data type is 0, the first input data is64-bit fixed-point data, and the primary processing circuit 101 and thesecondary processing circuits 102 of the operation unit 12 perform64-bit fixed-point operations without performing data type conversion.If the operation type identifier is 0000 and the flag bit indicating thedata type is 1, the first input data is 64-bit floating point data, andthe primary processing circuit 101 and the secondary processing circuits102 of the operation unit 12 perform 64-bit floating point operationswithout performing data type conversion. If the operation typeidentifier is 0001 and the flag bit indicating the data type is 0, thefirst input data is 32-bit fixed-point data, and the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 32-bit fixed-point operation without performing datatype conversion. If the operation type identifier is 0001 and the flagbit indicating the data type is 1, the first input data is 32-bitfloating point data, and the primary processing circuit 101 and thesecondary processing circuits 102 of the operation unit 12 perform32-bit floating point operation without performing data type conversion.If the operation type identifier is 0010 and the flag bit indicating thedata type is 0, the first input data is 16-bit fixed-point data, and theprimary processing circuit 101 and the secondary processing circuits 102of the operation unit 12 perform 16-bit fixed-point operation withoutperforming data type conversion. If the operation type identifier is0010 and the flag bit indicating the data type is 1, the first inputdata is 16-bit floating point data, and the primary processing circuit101 and the secondary processing circuits 102 of the operation unit 12perform 16-bit floating point operation without performing data typeconversion.

TABLE 6 Operation type Flag bit indicating data type identifier 0 1 0000The first input data is The first input data is 64-bit 64-bitfixed-point data floating point data and 64- and 64-bit fixed-point bitfloating point operations operations are are performed performed 0001The first input data is The first input data is 32-bit 32-bitfixed-point data floating point data and 32- and 32-bit fixed-point bitfloating point operations operations are are performed performed 0010The first input data is The first input data is 16-bit 16-bitfixed-point data floating point data and 16- and 16-bit fixed-point bitfloating point operations operations are are performed performed 0011The first input data is The first input data is 64-bit 64-bitfixed-point data, floating point data, the 64- the 64-bit fixed-pointbit floating point data is data is converted into converted into 64-bitfixed- 64-bit floating point point data, and 64-bit fixed- data, and64-bit floating point operations are point operations are performedperformed 0100 The first input data is The first input data is 32-bit32-bit fixed-point data, floating point data, the 32- the 32-bitfixed-point bit floating point data is data is converted into convertedinto 64-bit fixed- 64-bit floating point point data, and 64-bit fixed-data, and 64-bit floating point operations are point operations areperformed performed 0101 The first input data is The first input data is16-bit 16-bit fixed-point data, floating point data, the 16- the 16-bitfixed-point bit floating point data is data is converted into convertedinto 64-bit fixed- 64-bit floating point point data, and 64-bit fixed-data, and 64-bit floating point operations are point operations areperformed performed 0110 The first input data is The first input data is32-bit 32-bit fixed-point data, floating point data, the 32- the 32-bitfixed-point bit floating point data is data is converted into convertedinto 32-bit fixed- 32-bit floating point point data, and 32-bit fixed-data, and 32-bit floating point operations are point operations areperformed performed 0111 The first input data is The first input data is16-bit 16-bit fixed-point data, floating point data, the 16- the 16-bitfixed-point bit floating point data is data is converted into convertedinto 32-bit fixed- 32-bit floating point point data, and 32-bit fixed-data, and 32-bit floating point operations are point operations areperformed performed 1000 The first input data is The first input data is16-bit 16-bit fixed-point data, floating point data, the 16- the 16-bitfixed-point bit floating point data is data is converted into convertedinto 16-bit fixed- 16-bit floating point point data, and 16-bit fixed-data, and 16-bit floating point operations are point operations areperformed performed 1001 The first input data is The first input data is64-bit 64-bit fixed-point data, floating point data, the 64- the 64-bitfixed-point bit floating point data is data is converted into convertedinto 32-bit fixed- 32-bit floating point point data, and 32-bit fixed-data, and 32-bit floating point operations are point operations areperformed performed 1010 The first input data is The first input data is64-bit 64-bit fixed-point data, floating point data, the 64- the 64-bitfixed-point bit floating point data is data is converted into convertedinto 16-bit fixed- 16-bit floating point point data, and 16-bit fixed-data, and 16-bit floating point operations are point operations areperformed performed 1011 The first input data is The first input data is32-bit 32-bit fixed-point data, floating point data, the 32- the 32-bitfixed-point bit floating point data is data is converted into convertedinto 16-bit fixed- 16-bit floating point point data, and 16-bit fixed-data, and 16-bit floating point operations are point operations areperformed performed

If the operation type identifier is 0011 and the flag bit indicating thedata type is 0, the first input data is 64-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 64-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 64-bit floating point operations on the second inputdata. If the operation type identifier is 0011 and the flag bitindicating the data type is 1, the first input data is 64-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 64-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 64-bit fixed-point operations on the secondinput data.

If the operation type identifier is 0100 and the flag bit indicating thedata type is 0, the first input data is 32-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 64-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 64-bit floating point operations on the second inputdata. If the operation type identifier is 0100 and the flag bitindicating the data type is 1, the first input data is 32-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 64-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 64-bit fixed-point operations on the secondinput data.

If the operation type identifier is 0101 and the flag bit indicating thedata type is 0, the first input data is 16-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 64-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 64-bit floating point operations on the second inputdata. If the operation type identifier is 0101 and the flag bitindicating the data type is 1, the first input data is 16-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 64-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 64-bit fixed-point operations on the secondinput data.

If the operation type identifier is 0110 and the flag bit indicating thedata type is 0, the first input data is 32-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 32-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 32-bit floating point operations on the second inputdata. If the operation type identifier is 0110 and the flag bitindicating the data type is 1, the first input data is 32-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 32-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 32-bit fixed-point operations on the secondinput data.

If the operation type identifier is 0111 and the flag bit indicating thedata type is 0, the first input data is 16-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 32-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 32-bit floating point operations on the second inputdata. If the operation type identifier is 0111 and the flag bitindicating the data type is 1, the first input data is 16-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 32-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 32-bit fixed-point operations on the secondinput data.

If the operation type identifier is 1000 and the flag bit indicating thedata type is 0, the first input data is 16-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 16-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 16-bit floating point operations on the second inputdata. If the operation type identifier is 1000 and the flag bitindicating the data type is 1, the first input data is 16-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 16-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 16-bit fixed-point operations on the secondinput data.

If the operation type identifier is 1001 and the flag bit indicating thedata type is 0, the first input data is 64-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 32-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 32-bit floating point operations on the second inputdata. If the operation type identifier is 1001 and the flag bitindicating the data type is 1, the first input data is 64-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 32-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 32-bit fixed-point operations on the secondinput data.

If the operation type identifier is 1010 and the flag bit indicating thedata type is 0, the first input data is 64-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 16-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 16-bit floating point operations on the second inputdata. If the operation type identifier is 1010 and the flag bitindicating the data type is 1, the first input data is 64-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 16-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 16-bit fixed-point operations on the secondinput data.

If the operation type identifier is 1011 and the flag bit indicating thedata type is 0, the first input data is 32-bit fixed-point data. Theconversion unit 13 first converts the first input data into the secondinput data according to the decimal point position, and the second inputdata is 16-bit floating point data. And then, the primary processingcircuit 101 and the secondary processing circuits 102 of the operationunit 12 perform 16-bit floating point operations on the second inputdata. If the operation type identifier is 1011 and the flag bitindicating the data type is 1, the first input data is 32-bit floatingpoint data. The conversion unit 13 first converts the first input datainto the second input data according to the decimal position, and thesecond input data is 16-bit fixed-point data. And then, the primaryprocessing circuit 101 and the secondary processing circuits 102 of theoperation unit 12 perform 16-bit fixed-point operations on the secondinput data.

In an example, above-mentioned operation instruction may be afixed-point format operation instruction, and the fixed-point formatoperation instruction may include an opcode field and an opcode. Theopcode may be configured to indicate a function of the fixed-pointformat operation instruction. The opcode field may include a firstaddress of the first input data, the length of the first input data(optionally), a first address of output data, and a decimal pointposition.

If the above-mentioned controller unit 11 has obtained theabove-mentioned fixed-point format operation instruction, the controllerunit 11 parses the fixed-point format operation instruction, so as toobtain the first address of the first input data, the length of thefirst input data, the first address of the output data, and the decimalpoint position. Further, the controller unit 11 obtains the first inputdata from the storage unit 10 according to the first address of thefirst input data and the length of the first input data. And then, thecontroller unit 11 sends the first input data and the decimal pointposition to the conversion unit 13, and sends the first address of theoutput data to the operation unit 12. The conversion unit 13 may beconfigured to convert the first input data into the second input dataaccording to the decimal point position, and to transfer the secondinput data to the operation unit 12. The primary processing circuit 101and the secondary processing circuits 102 of the operation unit 12 maybe configured to perform operations on the second input data, so as toobtain the computation result of the computation instruction, and tostore the computation result of the computation instruction in aposition corresponding to the first address of the output data in thestorage unit 10.

In an example, before the operation unit 12 of the computation deviceperforms operations on data of an i^(th) layer of a multi-layer neuralnetwork model, the controller unit 11 of the computation device acquiresa configuration command, which may include a decimal point position anda data type of data involved in the operations. The controller unit 11parses the configuration instruction to obtain the decimal pointposition and the data type of the data involved in the operations.Alternatively, the controller unit 11 directly acquires theabove-mentioned decimal point position and the data type of the datainvolved in the operations from the storage unit 10. If the controllerunit 11 has obtained the input data, it is determined whether the datatype of the input data is consistent with that of the data involved inthe operations. If it is determined that the data type of the input datais inconsistent with that of the data involved in the operations, thecontroller unit 11 sends the input data, the decimal point position, andthe data type of the data involved in the operations to the conversionunit 13. The conversion unit 13 performs data type conversion on theinput data according to the decimal point position and the data type ofthe data involved in the operations, such that the data type of theinput data is consistent with that of the data involved in theoperations. And then, the input data converted is transferred to theoperation unit 12, and the primary processing circuit 101 and thesecondary processing circuits 102 of the operation unit 12 performoperations on the input data converted. If it is determined that thedata type of the input data is consistent with that of the data involvedin the operations, the controller unit 11 transfers the input data tothe operation unit 12, and the primary processing circuit 101 and thesecondary processing circuits 102 of the operation unit 12 directlyperform operations on the input data without performing data typeconversion.

In an example, if the input data is fixed-point data and the data typeof the data involved in the operations is fixed-point data, thecontroller unit 11 further determines whether the decimal point positionof the input data is consistent with the decimal point position of thedata involved in the operations. If the decimal point position of theinput data is inconsistent with the decimal point position of the datainvolved in the operations, the controller unit 11 transfers the inputdata, the decimal point position of the input data, and the decimalpoint position of the data involved in the operations to the conversionunit 13. The conversion unit 13 converts the input data into fixed-pointdata, and a decimal point position of the fixed-point data is consistentwith the decimal point position of the data involved in the operations.And then, the input data converted is transferred to the operation unit12, and the primary processing circuit 101 and the secondary processingcircuits 102 of the operation unit 12 perform operations on the inputdata converted.

In other words, the above-mentioned operation instruction may bereplaced with the above-mentioned configuration instruction.

In another example of the present disclosure, the operation instructionmay be an instruction of matrix multiplying matrix, an accumulationinstruction, an activation instruction, and the like.

In an example, as illustrated in FIG. 3E, the operation unit may includea tree module 40. The tree module 40 may include a root port 401 coupledwith the primary processing circuit 101 and multiple branch ports 402.Each of the multiple branch ports 402 is respectively coupled with oneof the multiple secondary processing circuits 102.

The tree module 40 may have a transceiving function. As illustrated inFIG. 3E, the tree module 40 may have a transferring function. Asillustrated in FIG. 6A, the tree module 40 may have a receivingfunction.

The tree module may be configured to forward data and the multipleoperation instructions exchanged among the primary processing circuit101 and the multiple secondary processing circuits 102.

The tree module 40 may be configured to forward data blocks, weights,and operation instructions exchanged among the primary processingcircuit 101 and the multiple secondary processing circuits 102.

In an example, the tree module 40 is an operation module of thecomputation device, and may include at least one layer of nodes. Eachnode is a line structure with a forwarding function and may not have acomputing function. If the tree module 40 has a zero layer of nodes, thetree module 40 may not be needed for the computation device.

In an example, the tree module 40 may be an n-tree structure, forexample, a binary tree structure as illustrated in FIG. 3F, and may alsobe a tri-tree structure. The n may be an integer greater than or equalto two. The examples of the present disclosure do not limit the specificvalue of the foregoing n. The number of layers may be two, and thesecondary processing circuit 102 may be coupled with nodes other thannodes of the second last layer. For example, the secondary processingcircuit 102 may be coupled with nodes of the first last layerillustrated in FIG. 3F.

In an example, the operation unit 12 may be provided with a separatecache. As illustrated in FIG. 3G, the operation unit 12 may include aneuron cache unit 63 configured to buffer input neuron vector data andoutput neuron weight data of the secondary processing circuits 102.

As illustrated in FIG. 3H, the operation unit 12 may further include aweight cache unit 64 configured to buffer weight data required by thesecondary processing circuit 102 in the operation process.

In an example, fully coupled operations in neural network operations aredescribed as an example, and the operation process may be expressed asy=f (wx+b), where x is an input neuron matrix, w is a weight matrix, bis an offset scalar, and f is an activation function. The activationfunction f may be one of sigmoid function, tanh function, relu function,and softmax function. In this example, assuming a binary tree structurewith 8 secondary processing circuits 102 is provided, which may beimplemented as follows:

obtaining, by the controller unit 11, the input neuron matrix x, theweight matrix w, and a fully coupled operation instruction from thestorage unit 10, and sending the input neuron matrix x, the weightmatrix w, and the fully coupled operation instruction to the primaryprocessing circuit 101;

dividing, by the primary processing circuit 101, the input neuron matrixx into eight sub-matrices, distributing the eight sub-matrices throughthe tree module to the eight secondary processing circuits 102, andbroadcasting the weight matrix w to the eight secondary processingcircuits 102; and

performing, by the plurality of secondary processing circuits 102,multiplication and accumulation operations of the eight sub-matrices andthe weight matrix w in parallel to obtain eight intermediate results,and sending the eight intermediate results to the primary processingcircuit 101.

The primary processing circuit 101 may be configured to rank eightinternal results to obtain an operation result, to perform an offsetoperation with the offset b on the operation result, and to perform anactivation operation to obtain a final result y. The final result y issent to the controller unit 11, and the controller unit 11 may beconfigured to output or store the final result y into the storage unit10.

In an example, the operation unit 12 may include, but not limited to,one or more multipliers of a first part, one or more adders of a secondpart (more specifically, the adders of the second part may alsoconstitute an addition tree), an activation function unit of a thirdpart, and/or a vector processing unit of the fourth part. Morespecifically, the vector processing unit may process vector operationsand/or pooling operations. In the first part, input data 1 (i.e., in1)multiplies input data 2 (i.e., in2) to obtain output data (i.e., out),the operations in the first part may be represented by out=in1*in2. Inthe second part, an addition operation is performed on the input data(in1) via the adder(s) to obtain the out data (out). More specifically,if the second part is an addition tree, an addition operation isperformed step by step on the input data (in1) through the addition treeto obtain the output data (out). The in1 is a vector of length N, and Nis greater than one, the operations in the second part may berepresented by out=in1[1]+in1[2]+ . . . +in1[N], and/or the input data(in1) is added step by step via the adder, and then added to the inputdata (in2) to obtain the output data (out), that is, out=in1[1]+in1[2]+. . . +in1[N]+in2. Alternatively, the input data (in1) and the inputdata (in2) is added to obtain the output data (out), that is,out=in1+in2. In the third part, operations of an activation function(active) may be performed on input data (in) to obtain an activationoutput data (out), and the operations in the third part may berepresented by out=active(in). The activation function (active) may beone of functions sigmoid, tanh, relu, softmax, etc. In addition to theactivation operation, the third part may implement other nonlinearfunctions. The input data (in) may be used to obtain the output data(out) via an operation function (0, that is, out=f (in). The vectorprocessing unit may be configured to perform the pooling operation onthe input data (in) to obtain the output data (out), that is,out=pool(in), where the function pool is a pooling operation. Thepooling operation may include, but not limited to, average pooling,maximum pooling, median pooling. The input data is data in a pooled coreassociated with output out.

The operation unit 12 may be configured to perform operations, which mayinclude at least one of operations in the first part, operations in thesecond part, operations in the third part, and operations in the fourthpart. The operations in the first part may include multiplying the inputdata 1 and the input data 2 to obtain the output data. In the operationsin the second part, an addition operation is performed (morespecifically, the addition operation is an addition tree operation, andwith the addition operation, the input data 1 is added step by step viathe addition tree), or the input data 1 is added with the input data 2to obtain the out data. In the third part, operations of the activationfunction (active) may be performed on the input data to obtain theoutput data. In the fourth part, the pooling operation may be performed,that is, out=pool (in), and the function pool is a pooling operation.The pooling operation may include, but is not limited to, averagepooling, maximum pooling, median pooling. The input data is data in apooled core associated with output out. The operations of theabove-identified parts may be freely selected by combining multipleparts in different sequences to achieve various functions. Accordingly,the operation unit 12 may include a secondary, tertiary, or quaternaryflow-level architecture.

It should be noted that the first input data is long-digitnon-fixed-point data, for example, 32-bit floating point data, and maybe standard 64-bit or 16-bit floating point data, etc. In this exampleof the present disclosure, only 32-bit data is described as an example.The second input data is short-digit fixed-point data, which is alsoreferred to as a small-digit fixed-point data, and representsfixed-point data represented by a smaller number of bits than the firstinput data of the long-digit non-fixed-point data.

In an example, the first input data is non-fixed-point data, and thesecond input data is fixed-point data, and a bit number occupied by thefirst input data is greater than or equal to that occupied by the secondinput data. For example, the first input data is 32-bit floating pointdata, and the second input data is 32-bit fixed-point data. For anotherexample, the first input data is 32-bit floating point data, and thesecond input data is 16-bit fixed-point data.

Specifically, for different layers of different network models, theabove-mentioned first input data may include different types of data.The decimal point positions of the different types of data aredifferent, that is, the accuracy of corresponding fixed-point data isdifferent from that of other data. For a fully coupled layer, the firstinput data may include data such as input neurons, weights, and offsetdata. For a convolution layer, the first input data may include datasuch as convolution kernels, input neurons, and offset data.

For example, for a fully coupled layer, the above-mentioned decimalpoint position may include a decimal point position of the input neuron,a decimal point position of the weight, and a decimal point position ofthe offset data. It should be noted that the decimal point position ofthe input neuron, the decimal point position of the weight, and thedecimal point position of the offset data may all be the same orpartially the same or different from each other.

In an example, the controller unit 11 may be further configured todetermine a decimal point position of the first input data and a bitwidth of the fixed-point data prior to acquiring the first input dataand the computation instruction. The bit width of fixed-point data is abit width of the first input data converted into fixed-point data.

The operation unit 12 may be further configured to initialize a decimalpoint position of the first input data and adjust a decimal pointposition of the first input data.

The bit width of fixed-point data of the first input data is bitsoccupied by the first input data represented by fixed-point data. Theabove-mentioned decimal point position is bits occupied by a fractionalpart of the first data represented by fixed-point data. The decimalpoint position may be configured to indicate the accuracy of thefixed-point data. Referring to the related description of FIG. 2A fordetails.

In an example, the first input data may be any type of data, and firstinput data a is converted into the second input data a according to thedecimal point position and the bit width of the fixed-point data, whichis described as follows.

$\hat{a} = \left\{ \begin{matrix}{{\left\lceil {a/2^{s}} \right\rceil*2^{s}},} & {{neg} \leq a \leq {pos}} \\{{pos},} & {a > {pos}} \\{{neg},} & {a < {neg}}\end{matrix} \right.$

If the first input data a satisfies a condition of neg≤a≤pos, the secondinput data â is represented as |a/2^(s)|*2^(s). If the first input dataa is greater than pos, the second input data â is represented as pos. Ifthe first input data a is less than neg, the second input data â isrepresented as neg.

In an example, input neurons, weights, output neurons, input neuronderivatives, output neuron derivatives, and weight derivatives of aconvolutional layer and a fully coupled layer are represented byfixed-point data.

In an example, the bit width of fixed-point data of the input neuron maybe 8, 16, 32, 64, or other bits. Specifically, the bit width offixed-point data of the input neuron is 8.

In an example, the bit width of fixed-point data of the weight may be 8,16, 32, 64, or other bits. Specifically, the bit width of thefixed-point data of the weight is 8.

In an example, the bit width of fixed-point data of the input neuronderivative may be 8, 16, 32, 64, or other bits. Specifically, the bitwidth of fixed-point data of the input neuron derivative is 16.

In an example, the bit width of fixed-point data of the output neuronderivative may be 8, 16, 32, 64, or other bits. Specifically, the bitwidth of fixed-point data of the output neuron derivative is 24 bits.

In an example, the bit width of fixed-point data of the weightderivative may be 8, 16, 32, 64, or other bits. Specifically, the bitwidth of fixed-point data of the weight derivative is 24 bits.

In an example, the data a with a large value in data involved in amulti-layer network model operation may adopt multiple representationmanners of fixed-point data. Referring to the related description ofFIG. 2B for details.

In an example, the first input data may be any type of data, and thefirst input data a is converted into the second input data a accordingto the decimal point position and the bit width of fixed-point data,which is described as follows.

$\hat{a} = \left\{ \begin{matrix}{{\sum_{i}^{n}{\hat{a}}_{i}},} & {{neg} \leq a \leq {pos}} \\{{pos},} & {a > {pos}} \\{{neg},} & {a < {neg}}\end{matrix} \right.$

If the first input data a satisfies a condition of neg≤a≤pos, the secondinput data â is represented as

${{\hat{a}}_{i} = {\left\lceil \frac{a - {\hat{a}}_{i} - 1}{2^{s\; i}} \right\rceil*2^{s\; i}}},$

where â₀=0. If the first input data a is greater than pos, the secondinput data â is represented as pos. If the first input data a is lessthan neg, the second input data â is represented as neg.

Further, the operation unit 12 may be configured to initialize thedecimal point position of the first input data as follows:

initializing the decimal point position of the first input dataaccording to a maximum absolute value of the first input data; or

initializing the decimal point position of the first input dataaccording to a minimum absolute value of the first input data; or

initializing the decimal point position of the first input dataaccording to a relationship between different data types in the firstinput data; or

initializing the decimal point position of the first input dataaccording to an empirical value constant.

Specifically, the above decimal point position S need to be initializedand dynamically adjusted according to data of different categories, dataof different neural network layers, and data of different iterationrounds.

An initialization process of the decimal point position of the firstinput data is specifically described as follows, for example, thedecimal point position adopted by the fixed-point data is determinedwhen converting the first input data at the first time.

The operation unit 1211 may be configured to initialize the decimalpoint position s of the first input data as follows:

initializing the decimal point position s of the first input dataaccording to the maximum absolute value of the first input data;initializing the decimal point positions of the first input dataaccording to the minimum absolute value of the first input data;initializing the decimal point positions of the first input dataaccording to relationship between different data types in the firstinput data; and initializing the decimal point position s of the firstinput data according to the empirical value constant.

Specifically, the above-mentioned initialization process is specificallydescribed as follows.

At step (a), the operation unit 12 may be configured to initialize thedecimal point position s of the first input data according to themaximum absolute value of the first input data.

The above-mentioned operation unit 12 specifically initializes thedecimal point positions of the first input data by performing anoperation shown by the following formula.

s _(a)=┌log₂ a _(max)−bitnum+1┐

The a_(max) represents the maximum absolute value of the first inputdata. The bitnum represents the bit width of the fixed-point dataconverted from the first input data. The s_(a) represents the decimalpoint position of the first input data.

According to categories and network levels, the data involved inoperations may be divided into the input neuron X^((l)), the outputneuron Y^((l)), the weight W^((l)), the input neuron derivative ∇_(X)^((l)), the output neuron derivative ∇_(Y) ^((l)), and the weightderivative ∇_(W) ^((l)) of the l-th layer. The maximum absolute valuemay be searched by one of manners including searching by data category,searching by layer and data category, searching by layer, data category,and group. The maximum absolute value of the first input data may bedetermined as follows.

At step (a.1), the operation unit 12 may be configured to search themaximum absolute value by data category.

Specifically, the first input data may include each element a_(i) ^((l))in a vector/matrix, where element a^((l)) may be an input neuronX^((l)), an output neuron Y^((l)), a weight W^((l)), an input neuronderivative ∇_(X) ^((l)), an output neuron derivative ∇_(Y) ^((l)), or aweight derivative ∇_(W) ^((l)). In other words, the above-mentionedfirst input data may include input neurons, weights, output neurons,input neuron derivatives, weight derivatives, and output neuronderivatives. The above-mentioned decimal point position of the firstinput data may include a decimal point position of the input neuron, adecimal point position of the weight, a decimal point position of theoutput neuron, a decimal point position of the input neuron derivative,a decimal point position of the weight derivative, and a decimal pointposition of the neuron derivative. The input neurons, the weights, theoutput neurons, the input neuron derivatives, the weight derivatives,and the output neuron derivatives are all represented in a matrix orvector form. The operation unit 12 may be configured to acquire themaximum absolute value (that is,

$a_{\max} = {\max\limits_{i,l}\left( {{abs}\left( a_{i}^{(l)} \right)} \right)}$

of each category data by traversing all elements in the vector/matrix ofeach layer of the above-mentioned multi-layer network model. The decimalpoint position s_(a) of the fixed-point data converted from the inputdata a of each category data is determined by a formula ofs_(a)=┌log_(a)a_(max)−bitnum+1┐.

At step (a.2), the operation unit 12 may be configured to search themaximum absolute value by layer and data category.

Specifically, the first input data may include each element a_(i) ^((l))in a vector/matrix, where a^((l)) may be input neurons X^((l)), outputneurons Y^((l)), weights , an input neuron derivative ∇_(X) ^((l)), anoutput neuron derivative ∇_(Y) ^((l)), or a weight derivative ∇_(w)^((l)). In other words, each layer of the above-mentioned multi-layernetwork model may include input neurons, weights, output neurons, inputneuron derivatives, weight derivatives, and output neuron derivatives.The above-mentioned decimal point position of the first input data mayinclude a decimal point position of the input neuron, a decimal pointposition of the weight, a decimal point position of the output neuron, adecimal point position of the input neuron derivative, a decimal pointposition of the weight derivative, and a decimal point position of theneuron derivative. The input neurons, the weights, the output neurons,the input neuron derivatives, the weight derivatives, and the outputneuron derivatives are all represented in a matrix or vector form. Theoperation unit 12 may be configured to acquire the maximum absolutevalue (that is,

$a_{{ma}\; x}^{(1)} = {\max\limits_{1}\left( {{abs}\left( a_{i}^{(1)} \right)} \right)}$

of each category data by traversing all elements in the vector/matrix ofeach data of each layer of the above-mentioned multi-layer networkmodel. The decimal point position s_(a) ^((l)) of the input data a ofeach category of l-th layer is determined by a formula of s^((l))_(a)=┌log₂a_(max) ^((l))−bitnum+1┐.

At step (a.3), the operation unit 12 may be configured to search themaximum absolute value by layer, data category, and group.

Specifically, the first input data may include each element a_(i) ^((l))in a vector/matrix, where a^((l)) may be input neurons X^((l)), outputneurons Y^((l)), weights W^((l)), an input neuron derivative ∇_(X)^((l)), an output neuron derivative ∇_(Y) ^((l)), or a weight derivative∇_(W) ^((l)). In other words, data categories of each layer of theabove-mentioned multi-layer network model may include input neurons,weights, output neurons, input neuron derivatives, weight derivatives,and output neuron derivatives. The above-mentioned operation unit 12divides data of each category of each layer of the above-mentionedmulti-layer network model into g groups, or into groups by otherarbitrary grouping rules. The operation unit 12 then traverses eachelement of each group of data in the g groups data corresponding to thedata of each category of each layer of the above-mentioned multi-layernetwork model, and obtains the element with the largest absolute value(that is,

$a_{\max}^{({g,l})} = {\max\limits_{l}\left( {{abs}\left( a_{i}^{({l,g})} \right)} \right)}$

in the group of data. The decimal point position s_(a) ^((l,g)) of dataof each group of the g groups data corresponding to each category ofeach layer is determined by a formula of s_(a) ^((l,g))=┌log₂(a_(max)^((l,g)))−bitnum+1┐.

The foregoing arbitrary grouping rules may include, but not limited to,rule for grouping according to data ranges, rule for grouping accordingto data training batches, and the like.

At step (b), the above-mentioned operation unit 12 initializes thedecimal point position s of the first input data according to theminimum absolute value of the first input data.

Specifically, the above-mentioned operation unit 12 determines theminimum absolute value a_(min) of data to be quantized, and determinesthe fixed-point precision s by the following formula.

s_(a)=└log2(a_(min))┘

The above-mentioned a_(min) is the minimum absolute value of the firstinput data. For the process of acquiring a_(min), please refer to theabove-identified steps (a.1), (a.2), and (a.3).

At step (c), the above-mentioned operation unit 12 initializes thedecimal point position s of the first input data according to therelationship between different data types in the first input data.

Specifically, the decimal point position s_(a) ^((l)) of the data typea^((l)) of any layer (such as the first layer) of the multi-layernetwork model is determined by the above-mentioned operation unit 12according to the decimal point position s_(b) ^((l)) of the data typeb^((l)) of the first layer and a formula of s_(a)^((l))=Σ_(b≈a)α_(a)s_(b) ^((l))+β_(b).

The a^((l)) and b^((l)) may be input neurons X^((l)), output neuronsY^((l)), weights W^((l)), input neuron derivatives ∇_(X) ^((l)), outputneuron derivatives ∇_(Y) ^((l)), or weight derivatives ∇_(W) ^((l)). Thea^((l)) and b^((l)) are integer constants.

At step (d), the above-mentioned operation unit 12 initializes thedecimal point position s of the first input data according to theempirical value constant.

Specifically, the decimal point position s_(a) ^((l)) of the data typea^((l)) of any layer (such as the first layer) of the multi-layernetwork model may be artificially set as s_(a) ^((l))=c, where c is aninteger constant. The above-mentioned a^((l)) may be input neuronsX^((l)), output neurons Y^((l)), weights W^((l)), input neuronderivatives ∇_(X) ^((l)), output neuron derivatives ∇_(Y) ^((l)), orweight derivatives ∇_(W) ^((l)).

Further, an initialization value of the decimal point position of theinput neuron and an initialization value of the decimal point positionof the output neuron may be selected within the range of [−8, 8]. Aninitialization value of the decimal point position of the weight may beselected within the range of [−17, 8]. An initialization value of thedecimal point position of the input neuron derivative and aninitialization value of the decimal point position of the output neuronderivative may be selected within the range of [−40, −20]. Aninitialization value of the decimal point position of the weightderivative may be selected within the range of [−48, −12].

The method of dynamically adjusting the above-mentioned decimal pointposition of the data by the above-mentioned operation unit 12 will bespecifically described as follows.

The method of dynamically adjusting the above-mentioned decimal pointposition s by the above-mentioned operation unit 12 may includeadjusting the above-mentioned decimal point position s upwards (that is,value of the decimal point position s becomes larger) and adjusting theabove-mentioned decimal point position s downwards (that is, value ofthe decimal point position s becomes smaller). Specifically, theabove-mentioned decimal point position s is adjusted upwardly by asingle step according to the maximum absolute value of the first inputdata. The above-mentioned decimal point position s is adjusted upwardlystep by step according to the maximum absolute value of the first inputdata. The above-mentioned decimal point position s is adjusted upwardlyby a single step according to the first input data distribution. Theabove-mentioned decimal point position s is adjusted upwardly step bystep according to the first input data distribution. The above-mentioneddecimal point position s is adjusted downwardly according to theabsolute value of the first input date.

In case (a), the above-mentioned operation unit 12 may be configured toupwardly adjust the above-mentioned decimal point position s by a singlestep according to the maximum absolute value of the first input data.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). If themaximum absolute value a_(max) of the data in the first input data isgreater than and equal to pos, the decimal point position adjusted iss_new=┌log₂a_(max)−bitnum+1┐; otherwise, the above-mentioned decimalpoint position will not be adjusted, that is, s_new=s_old.

In case (b), the above-mentioned operation unit 12 may be configured toupwardly adjust the above-mentioned decimal point position s step bystep according to the maximum absolute value of the first input data.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). If themaximum absolute value a_(max) of the data in the first input data isgreater than and equal to pos, the decimal point position adjusted iss_new=s_old+1; otherwise, the above-mentioned decimal point positionwill not be adjusted, that is s_new=s_old.

In case (c), the above-mentioned operation unit 12 may be configured toupwardly adjust the above-mentioned decimal point position s by a singlestep according to the first input data distribution.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old).Statistical parameters of the absolute value of first input data areprocessed, such as a mean value a_(mean) of the absolute value and astandard deviation a_(std) of the absolute value. The maximum range ofdata is set as a_(max)=a_(mean)+na_(std). If a_(max)≥pos,s_new=┌log₂a_(max)−bitnum+1┐; otherwise, the above-mentioned decimalpoint position will not be adjusted, that is, s_new=s_old.

Further, the above-mentioned n may be two or three.

In case (d), the above-mentioned operation unit 12 may be configured toupwardly adjust the above-mentioned decimal point position s step bystep according to the first input data distribution.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). Thestatistical parameters of the absolute value of first input data areprocessed, such as the mean value a_(mean) of the absolute value and thestandard deviation a_(std) of the absolute value. The maximum range ofdata is set as a_(max)=a_(mean)+na_(std), where n is three. Ifa_(max)≥pos, s_new=s_old+1; otherwise, the above-mentioned decimal pointposition will not be adjusted, that is s_new=s_old.

In case (e), the above-mentioned operation unit 12 may be configured todownwardly adjust the above-mentioned decimal point position s accordingto the absolute value of the first input date.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)=1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). If themaximum absolute value a_(max) of the first input date is less2^(s_old+(bitnum−n)) and s_old≥s_(min), s_new=s_old, where the n is aninteger constant, and the s_(min) may be an integer or a negativeinfinity.

Further, the above-mentioned n is three, and the above-mentioned s_(min)is −64.

In an example, an adjusting frequency of the decimal point position maybe determined as follows. The decimal point position of the first inputdata may never be adjusted. Alternatively, the decimal point position ofthe first input data may be adjusted every n first training cycles(i.e., iteration), where the n is a constant. Alternatively, the decimalpoint position of the first input data may be adjusted every n secondtraining cycles (i.e., epoch), where the n is a constant. Alternatively,the decimal point position of the first input data may be adjusted everyn first training periods or n second training periods, and adjusting thedecimal point position of the first input data every n first trainingperiods or second training periods, and then the n may be adjusted, thatis, n=αn, where α is greater than one. Alternatively, the decimal pointposition of the first input data may be adjusted every n first trainingperiod or second training period, and the n is gradually decreased asthe number of training rounds increases.

Further, the decimal point position of the input neuron, the decimalpoint position of the weight, and the decimal point position of theoutput neuron may be adjusted every 100 first training cycles. Thedecimal point position of the input neuron derivative and the decimalpoint position of the output neuron derivative may be adjusted every 20first training cycles.

It should be noted that the first training period is the time requiredto train a batch of samples, and the second training period is the timerequired to perform training for all training samples.

In an example, if the controller unit 11 or the operation unit 12obtains the decimal point position of the first input data according tothe above-identified process, the decimal point position of the firstinput data is stored in the cache 202 of the storage unit 10.

If the above-mentioned computation instruction is an immediate addressinstruction, the primary processing unit 101 directly converts the firstinput data into the second input data according to the decimal pointposition indicated by the opcode field of the computation instruction.If the above-mentioned computation instruction is a direct addressinginstruction or an indirect addressing instruction, the primaryprocessing unit 101 obtains the decimal point position of the firstinput data according to the storage space indicated by the opcode fieldof the computation instruction. The first input data is then convertedto the second input data according to the decimal point position.

The above-mentioned computation device may also include a rounding unit.During the operation, the accuracy of the operation result obtained byadding, multiplying, and/or other operations on the second input data(the operation result may include the intermediate results and thecomputation result of the computation instruction) exceeds the accuracyrange of the current fixed-point data. Therefore, a data cache unit maybe configured to buffer the above-mentioned intermediate results. Afterthe end of the operation, the rounding unit performs a roundingoperation on the operation result exceeding the precision range of thefixed-point data, and obtains the rounded operation result. After theoperation is completed, the rounding unit rounds the operation resultexceeding the precision range of the fixed-point data, and obtains arounded operation result. The data conversion unit then converts therounded operation result into data of the type of the currentfixed-point data.

Specifically, the rounding unit performs a rounding operation on theintermediate results, where the rounding operation may be any one of arandom rounding operation, a rounding-off operation, a rounding-upoperation, a rounding-down operation, and a truncation roundingoperation.

If the rounding unit performs the random rounding operation, therounding unit performs the following operations.

$y = \left\{ \begin{matrix}\left\lfloor x \right\rfloor & {{{w.p}{.1}} - \frac{x - \left\lfloor x \right\rfloor}{ɛ}} \\{\left\lfloor x \right\rfloor + ɛ} & {w.p.\frac{x - \left\lfloor x \right\rfloor}{ɛ}}\end{matrix} \right.$

The Y represents data obtained by performing random rounding operationon the operation result x before rounding, that is, the above-mentionedrounded operation result. The ϵ represents the minimum positive numberthat may be represented by the current fixed-point data representationformat, that is, 2^(−PointLocation). The └x┘ represents data obtained bydirectly truncating the fixed-point data from the operation result xbefore rounding (similar to rounding down the decimal). The w.p.represents a probability. A probability expressed by theabove-identified formula of obtaining the data └x┘ by performing randomrounding operation on the operation result x before rounding is

$1 - {\frac{x - \left\lfloor x \right\rfloor}{ɛ}.}$

A probability of obtaining data └x┘+ϵ by performing random roundingoperation on the intermediate result x before rounding is

$\frac{x - \left\lfloor x \right\rfloor}{ɛ}.$

If the rounding unit performs the rounding-off operation, the roundingunit performs the following operations.

$y = \left\{ \begin{matrix}\left\lfloor x \right\rfloor & {{{if}\mspace{14mu} \left\lfloor x \right\rfloor} \leq x \leq {\left\lfloor x \right\rfloor + \frac{ɛ}{2}}} \\{\left\lfloor x \right\rfloor + ɛ} & {{{{if}\mspace{14mu} \left\lfloor x \right\rfloor} + \frac{ɛ}{2}} \leq x \leq {\left\lfloor x \right\rfloor + ɛ}}\end{matrix} \right.$

The Y represents data obtained by performing rounding-off operation onthe operation result x before rounding, that is, the above-mentionedrounded operation result. The ϵ represents the minimum positive numberthat may be represented by the current fixed-point data representationformat, that is, 2^(−Point Location). The └x┘ is an integral multiple ofthe ϵ, and a value of the └x┘ is a maximum number less than or equal tothe x. If operation result x before rounding satisfies a condition of

${\left\lfloor x \right\rfloor \leq x \leq {\left\lfloor x \right\rfloor + \frac{ɛ}{2}}},$

the above-mentioned rounded operation result is └x┘. If operation resultx before rounding satisfies a condition of

${{\left\lfloor x \right\rfloor + \frac{ɛ}{2}} \leq x \leq {\left\lfloor x \right\rfloor + ɛ}},$

the above-mentioned rounded operation result is └x┘+ϵ.

If the rounding unit performs the rounding-up operation, the roundingunit performs the following operations.

y=┌x┐

The y represents data obtained by performing rounding-up operation onthe operation result x before rounding, that is, the above-mentionedrounded operation result. The ┌x┐ is an integral multiple of the ϵ, anda value of the └x┘ is a maximum number less than or equal to the x. Theϵ represents the minimum positive number that may be represented by thecurrent fixed-point data representation format, that is,2^(−Point Location).

If the rounding unit performs the rounding-down operation, the roundingunit performs the following operations.

y=└x┘

The y represents data obtained by performing rounding-up operation onthe operation result x before rounding, that is, the above-mentionedrounded operation result. The └x┘ is an integral multiple of the ϵ, anda value of the └x┘ is a maximum number less than or equal to the x. Theϵ represents the minimum positive number that may be represented by thecurrent fixed-point data representation format, that is,2^(−Point Location).

If the rounding unit performs the truncation rounding operation, therounding unit performs the following operations.

y=[x]

The y represents data obtained by performing truncation roundingoperation on the operation result x before rounding, that is, theabove-mentioned rounded operation result. The [x] represents dataobtained by directly truncating the fixed-point data from the operationresult x.

If the rounding unit obtains an intermediate result after the rounding(that is, a rounded intermediate result), the operation unit 12 convertsthe rounded intermediate result into data of the type of the currentfixed-point data according to the decimal point position of the firstinput data.

In an example, the operation unit 12 does not perform truncationprocessing on at least one intermediate result whose data type isfloating point type.

The secondary processing circuits 102 of the operation unit 12 performoperations to obtain the intermediate results according to theabove-identified method. Since there are operations such asmultiplication and division, which cause the intermediate resultsobtained to exceed the memory storage range, resulting that theintermediate results exceeding the memory storage range are generallytruncated. However, since the intermediate results generated during theoperation process of the present disclosure are not stored in thememory, it is not necessary to truncate the intermediate resultsexceeding the memory storage range, thereby greatly reducing theprecision loss of the intermediate results and improving the accuracy ofthe computation result.

In an example, the operation unit 12 may further include a derivationunit. If the operation unit 12 receives the decimal point position ofthe input data involved in the fixed-point operation, the derivationunit derives a decimal point position of the at least one intermediateresult obtained during the fixed-point operation according to thedecimal point position of the input data involved in the fixed-pointoperation. If the intermediate result obtained by operation subunitsexceeds the range indicated by the corresponding decimal point position,the derivation unit shifts the decimal point position of theintermediate result to the left by M bits, such that the accuracy of theintermediate result is within a precision range indicated by the decimalpoint position of the intermediate result, and the M is an integergreater than zero.

For example, the first input data may include the input data I1 and theinput data I2, and the corresponding decimal point positions are P1 andP2, and P1>P2. If the operation type indicated by the above-mentionedoperation instruction is an addition operation or a subtractionoperation, that is, if the operation subunit performs the operation ofI1+I2 or I1−I2, the derivation unit derives a decimal point position ofthe intermediate result of the operation process indicated by theoperation instruction as P1. If the operation type indicated by theoperation instruction is a multiplication operation, that is, if theoperation subunit performs an I1*I2 operation, the derivation unitderives a decimal point position of the intermediate result of theoperation process indicated by the operation instruction as P1*P2.

In an example, the operation unit 12 may further include a data cacheunit for buffering the at least one intermediate result.

In an example, the computation device may further include a datastatistical unit. The data statistical unit may be configured to performstatistics on the input data of the same type of the each layer in themulti-layer network model, so as to obtain the decimal point position ofeach type of each layer of the multi-layer network model.

The data statistical unit may also be part of an external device. Thecomputation device obtains a decimal point position of data involvingoperations from the external device before performing the dataconversion.

Specifically, the data statistical unit may include an acquiringsubunit, a statistical subunit, and an analysis subunit.

The acquiring subunit may be configured to extract the input data of thesame type of the each layer in the multi-layer network model.

The statistical subunit may be configured to compute and obtain adistribution ratio of the input data of the same type of the each layerin the multi-layer network model on a preset interval;

The analysis subunit may be configured to obtain a decimal pointposition of the input data of the same type of the each layer in themulti-layer network model according to the distribution ratio.

The preset interval may be └−2^(X−1−i),2^(X−1−i)−2^(−i)┘, i=0,1,2, . . ., n, n is a preset positive integer, and X is a bit number occupied bythe fixed-point data. The above-mentioned preset interval└−2^(Z−1−i),2^(X−1−i)−2^(−i)┘ may include n+1 subintervals. Thestatistical subunit may be configured to count distribute information ofthe input data of the same type of the each layer in the multi-layernetwork model on the n+1 subintervals, and to obtain a firstdistribution ratio according to the distribution information. The firstdistribution ratio is p₀, p₁, p₂, . . . , p_(n). The n+1 values of thefirst distribution ratio distribution are ratios of the input data ofthe same type of the each layer in the multi-layer network model in then+1 subintervals. The analysis subunit sets an overflow rate EPL inadvance, and obtains a maximum value i from 0,1,2, . . . ,n, such thatp_(i)≥1−EPL. The maximum value i is the decimal point position of theinput data of the same type of the each layer in the multi-layer networkmodel. In other words, the analysis subunit takes the decimal pointposition of the input data of the same type of the each layer in themulti-layer network model as max{i/p_(i)≥1−EPL, i∈{0,1,2, . . . , n}},that is, a largest subscript value i is selected as the decimal pointposition of the input data of the same type of the each layer in themulti-layer network model from the p_(i) which is greater than or equalto 1−EPL.

It should be noted that, the p_(i) is a ratio of the number of the inputdata of the same type of the each layer in the multi-layer network modelwhose value ranged in the interval └−2^(X−1−i),2^(X−1−i)−2^(−i)┘ to thetotal number of the input data of the same type of the each layer in themulti-layer network model. For example, there are ml input data of thesame type of the each layer in the multi-layer network model, and m2input data of the m1 input data are in the interval └−2^(X-1-i),2^(X-1-i)−2^(−i)┘, and then

$p_{i} = {\frac{m\; 2}{m\; 1}.}$

In an example, in order to improve the operation efficiency, theacquiring subunit may be configured to randomly or sample extract partof the input data of the same type of the each layer in the multi-layernetwork model, to acquire the decimal point position of the part of theinput data according to the above-identified method, and to perform dataconversion on the decimal point position of the part of the input data(including conversion of floating point data into fixed-point data,conversion of fixed-point data into fixed-point data, conversion offixed-point data into fixed-point data, etc.), thereby improving thecomputation speed and efficiency under the premise of maintainingaccuracy.

In an example, the data statistical unit may determine the bit width andthe decimal point position of the input data of the same type or theinput data of the same layer according to a median value of the inputdata of the same type or the input data of the same layer.Alternatively, the data statistical unit may determine the bit width andthe decimal point position of the input data of the same type or theinput data of the same layer according to an average value of the inputdata of the same type or the input data of the same layer.

In an example, if the operation unit may be configured to performoperations on the input data of the same type or the input data of thesame layer to obtain an intermediate result, and the intermediate resultexceeds a value range corresponding to the decimal point position andthe bit width of the input data of the same type or the input data ofthe same layer, the operation unit does not perform truncationprocessing on the intermediate result, and caches the intermediateresult in the data cache unit of the operation unit for subsequentoperations.

Specifically, the opcode field may include a decimal point position andan identifier of data type conversion of the input data. The instructionprocessing unit parses the data conversion instruction to obtain thedecimal point position and the identifier of data type conversion of theinput data. The instruction processing unit may further include a dataconversion unit, and the data conversion unit converts the first inputdata into the second input data according to the decimal point positionand the identifier of data type conversion of the input data.

It should be noted that the above-mentioned network model may includemultiple layers, such as a fully coupled layer, a convolution layer, apooling layer, and an input layer. In the above-mentioned at least oneinput data, the input data belonging to the same layer has the samedecimal point position, that is, the above-mentioned input data of thesame layer shares the same decimal point position.

The above-mentioned input data may include different types of data, suchas input neurons, weights, and offset data. The input data of the sametype in the input data has the same decimal point position, that is, theabove-mentioned input data of the same type shares the same decimalpoint position.

For example, if the operation type indicated by the operationinstruction is a fixed-point operation, and the input data involved inthe operation indicated by the operation instruction is floating pointdata, the data conversion unit converts the input data from the floatingpoint data to fixed-point data before performing the fixed-pointoperation. For another example, if the operation type indicated by theoperation instruction is a floating point operation, and the input datainvolved in the operation indicated by the operation instruction isfixed-point data, the data conversion unit converts the input datacorresponding to the operation instruction from fixed-point data tofloating point data before performing the floating point operation.

For macro instructions (such as the computation instruction and the dataconversion instruction) involved in the present disclosure, thecontroller unit 11 may parse the macro instruction to obtain an opcodefield and an opcode of the macro instruction. A micro instructioncorresponding to the macro instruction is generated according to theopcode field and the opcode. Alternatively, the controller unit 11decodes the macro instruction to obtain the micro instructioncorresponding to the macro instruction.

In an example, a main processor and a coprocessor are included in asystem on chip (SOC), and the main processor may include theabove-mentioned computation device. The coprocessor obtains the decimalpoint position of input data of the same type of the each layer in themulti-layer network model according to the above-identified method, andsends the decimal point position of the input data of the same type ofthe each layer in the multi-layer network model to the computationdevice. Alternatively, if the computation device needs to use thedecimal point position of the input data of the same type of the eachlayer in the multi-layer network model, the decimal point position ofthe input data of the same type of the each layer in the multi-layernetwork model is obtained from the above-mentioned coprocessor.

In an example, the first input data is non-fixed-point data, and thenon-fixed-point data may include long-digit floating point data,short-digit floating point data, integer data, and discrete data.

The data types of the above-mentioned first input data are differentfrom each other. For example, the input neurons, weights, and offsetdata are floating point data. Alternatively, some of the input neurons,weights, and offset data are floating point data, and some of the inputneurons, weights, and offset data are integer data. Alternatively, theabove-mentioned input neurons, weights, and offset data are all integerdata. The computation device may realize the conversion ofnon-fixed-point data to fixed-point data, that is, the computationdevice may realize the conversion of data such as long- digit floatingpoint data, short-digit floating point data, integer data, and discretedata to fixed-point data. The fixed-point data may be signed fixed-pointdata or unsigned fixed-point data.

In an example, the first input data and the second input data are bothfixed-point data, and the first input data and the second input data mayboth be signed fixed-point data, or both are unsigned fixed-point data.Alternatively, one of the first input data and the second input data isunsigned fixed-point data and the other is signed fixed-point data. Andthe decimal point position of the first input data is different fromthat of the second input data.

In an example, the first input data is fixed-point data, and the secondinput data is non-fixed-point data. In other words, the computationdevice may realize the conversion of fixed-point data to non-fixed-pointdata.

FIG. 4 is a flow chart of a forward operation of a single-layer neuralnetwork according to an example of the present disclosure. The flowchart depicts a forward operation process of a single layer neuralnetwork implemented by using the computation device and an instructionset of the present disclosure. For each layer, an input neuron vector isfirst weighted and summed to compute an intermediate result vector ofthe layer. The intermediate result vector is offset and activated toobtain an output neuron vector. The output neuron vector is used as aninput neuron vector of the next layer.

In a specific application scenario, the computation device may be atraining device. Before training the neural network model, the trainingdevice may be configured to acquire training data involved in a trainingof the neural network model, and the training data is non-fixed-pointdata. The training device may be further configured to obtain decimalpoint position of the training data according to the above-identifiedmethod. The training device converts the training data into trainingdata represented by fixed-point data according to the decimal pointposition of the training data. The training device performs a forwardneural network operation based on the training data represented byfixed-point data to obtain a neural network operation result. Thetraining device performs a random rounding operation on the neuralnetwork operation result exceeding a range of data precision representedby the decimal point position of the training data, to obtain a roundedneural network operation result, where the neural network operationresult is located in the range of data precision represented by thedecimal point position of the training data. According to theabove-identified method, the training device acquires the neural networkoperation result (that is, the output neurons) of each layer of themulti-layer neural network. The training device obtains an output neurongradient according to the output neurons of each layer, and performs areverse operation according to the output neuron gradient to obtain aweight gradient, thereby updating the weights of the neural networkmodel according to the weight gradient.

The above-mentioned training device repeatedly performs theabove-identified process to achieve the purpose of training the neuralnetwork model.

It should be noted that, before the forward operation and a reversetraining, the computation device performs data conversion on the datainvolved in the forward operation, and does not perform data conversionon the data involved in the reverse training. Alternatively, thecomputation device does not perform data conversion on data involved inthe forward operation, and performs data conversion on the dataparticipating in the reverse training. Alternatively, the computationdevice performs data conversion on data involved in the reverse trainingand on data involved in the forward operation. The specific dataconversion process may be referred to the description of theabove-mentioned related examples, and will not be described herein.

The forward operation may include the above-mentioned multi-layer neuralnetwork operation, and the multi-layer neural network operation mayinclude operations such as convolution, which is implemented by aconvolution operation instruction.

The above-mentioned convolution operation instruction may be aninstruction in the Cambricon instruction set. The Cambricon instructionset is characterized in that the instruction may include an opcode andoperands. The instruction set may include four types of instructions,that is, control instructions, data transfer instructions, operationinstructions, and logical instructions.

Preferably, each instruction in the instruction set has a fixed length.For example, each instruction in the instruction set may be 64 bits inlength.

Further, the control instructions may be configured to control anexecution process. The control instructions include jump instructionsand conditional branch instructions.

Further, the data transfer instructions may be configured to completedata transfer between different storage media. The data transferinstruction may include a load instruction, a store instruction, and amove instruction. The load instruction may be configured to load datafrom a main memory to a cache, the store instruction may be configuredto store data from the cache to the main memory, and the moveinstruction may be configured to transfer data between the cache and thecache, or between the cache and registers, or between registers andregisters. The data transfer instructions support three different waysof organizing data, which include matrices, vectors, and scalars.

Further, the operation instructions may be configured to perform neuralnetwork operations. The operation instruction may include a matrixoperation instruction, a vector operation instruction, and a scalaroperation instruction.

Furthermore, the matrix operation instruction performs matrix operationsin neural networks, which include operations of matrix multiplyingvector, vector multiplying matrix, and matrix multiplying scalar, outerproducts, matrix adding matrix, matrix subtracting matrix.

Furthermore, the vector operation instruction performs vector operationsin the neural networks, which include operations of vector elementaryarithmetic, vector transcendental functions, dot products, random vectorgenerators, and maximum/minimum of a vector). The elementary arithmeticinclude vector addition, vector subtraction, vector multiplication, andvector division. The vector transcendental functions are functions thatdo not satisfy any polynomial equations that use polynomials ascoefficients, including but not limited to exponential functions,logarithmic functions, trigonometric functions, and inversetrigonometric functions.

Furthermore, the scalar operation instruction performs scalar operationsin the neural networks, including scalar elementary arithmetic andscalar transcendental functions. The scalar basic operations includescalar addition, scalar subtraction, scalar multiplication, and scalardivision, the scalar transcendental functions are functions that do notsatisfy any polynomial equations that use polynomials as coefficients,including but not limited to exponential functions, logarithmicfunctions, trigonometric functions, and inverse trigonometric functions.

Further, the logical instructions may be configured to perform logicaloperations in the neural networks. The logical operations include vectorlogical operation instructions and scalar logical operationinstructions.

Furthermore, the vector logical operation instruction may include avector comparison, a vector logical operation, and a vector greater thanmerge. The vector comparison may include, but are not limited to,greater than, less than, equal to, greater than or equal to, less thanor equal to, and not equal to. The vector logical operations includewith, or, not.

Further, the scalar logical operations may include scalar comparison,scalar logical operations. The scalar comparison may include, but is notlimited to, greater than, less than, equal to, greater than or equal to,less than or equal to, and not equal to. The scalar logical operationsinclude AND, OR, and NOT.

For a multi-layer neural network, the implementation process may beexecuted as follows. In the forward operation, if the forward operationof a previous layer artificial neural network is completed, operationinstructions of a next layer will operate the output neuron processed inthe operation unit as the input neuron of the next layer (or performsome operations on the output neuron, and then the output neuron isoperated as the input neuron of the next layer). At the same time, theweight is also replaced by the weight of the next layer. In the reverseoperation, if the reverse operation of a previous artificial neuralnetwork is completed, operation instructions of a next layer willoperate an input neuron gradient processed in the operation unit as anoutput neuron gradient of the next layer (or perform some operations onthe input neuron gradient, and then the input neuron gradient isoperated as the output neuron gradient of the next layer). At the sametime, the weight is also replaced by the weight of the next layer. Asillustrated in FIG. 5, arrows of the broken line in FIG. 5 indicate thereverse operation, and arrows of the solid line indicate the forwardoperation.

In another example, the operation instruction may be a matrixmultiplying matrix instruction, an accumulation instruction, anactivation instruction, and the like. The operation instruction mayinclude a forward operation instruction and a direction traininginstruction.

The specific computation method of the computation device illustrated inFIG. 3A is explained herein via the neural network operationinstruction. For the neural network operation instruction, the formulathat it actually needs to execute may be s=s(Σwx_(i)+b), where theweight w is multiplied by the input data, then a summation operation isperformed, and then the offset b is added to perform the activationoperation s(h) to obtain the final output result s.

The method for performing the neural network forward operationinstruction by the computation device is illustrated in FIG. 3A, whichmay include the following.

If the conversion unit 13 has performed data type conversion on thefirst input data, the controller unit 11 extracts the neural networkforward operation instruction, and an opcode field and at least oneopcode corresponding to the neural network operation instruction fromthe instruction cache unit 110, and the controller unit 11 sends theopcode field to the data access unit, and sends the at least one opcodeto the operation unit 12.

The controller unit 11 extracts the weight w and the offset bcorresponding to the opcode field from the storage unit 10 (if b iszero, the offset b does not need to be extracted). The weight w and theoffset b are sent to the primary processing circuit 101 of the operationunit, and the controller unit 11 extracts the input data Xi from thestorage unit 10, and sends the input data Xi to the primary processingcircuit 101.

The primary processing circuit 101 divides the input data Xi into n datablocks.

The instruction processing unit 111 of the controller unit 11 determinesa multiplication instruction, an offset instruction, and an accumulationinstruction according to the at least one opcode, and sends themultiplication instruction, the offset instruction, and the accumulationinstruction to the primary processing circuit 101. The primaryprocessing circuit 101 broadcasts the multiplication instruction and theweight w to the multiple secondary processing circuits 102, anddistributes the n data blocks to the multiple secondary processingcircuits 102 (for example, there are n secondary processing circuits102, then each secondary processing circuit 102 is distributed with onedata block). The multiple secondary processing circuits 102 performmultiplication operations on the data blocks received and the weight wto obtain intermediate results according to the multiplicationinstruction, and send the intermediate results to the primary processingcircuit 101. The primary processing circuit 101 performs an accumulationoperation on the intermediate results sent by the multiple secondaryprocessing circuits 102 to obtain an accumulation result according tothe accumulation instruction, and performs an addition operation on theaccumulation result and the offset b to obtain the final resultaccording to the offset instruction, and sends the final result to thecontroller unit 11.

In addition, the order of the addition operation and the multiplicationoperation may be reversed.

It should be noted that the method for performing the neural networkreverse training instruction by the above-mentioned computation deviceis similar to a method for performing the neural network forwardoperation instruction by the above-mentioned computation device.Specific details may refer to the related description of the reversetraining, which are not described herein.

The technical solution provided by the present application may realizethe multiplication operation and the offset operation of the neuralnetwork through an instruction (that is, the neural network operationinstruction), and the intermediate results of the neural networkoperation may be performed without storage and extraction operations,which may reduce the storage and extraction operations of theintermediate data. Therefore, the technical solution provided by thepresent application may reduce the corresponding operational steps andimprove the computational effect of the neural network.

A machine learning operation device may be further provided. The machinelearning operation device may include one or more computation devicesmentioned in the present disclosure for acquiring data to be processedand control information from other processing devices, performingspecified machine learning computations, and sending execution resultsto peripheral devices through I/O interfaces. The peripherals includecameras, monitors, mice, keyboards, network cards, WIFI interfaces,servers, and the like. If multiple computation devices are provided, thecomputation devices may link and transfer data with each other through aspecific structure. For example, data may be interconnected andtransmitted via the PCIE bus, so as to support larger scale machinelearning computations. In this case, the multiple computation devicesmay share the same control system, or have separate control systems.Further, the multiple computation devices may share the same memory, oreach accelerator may have its own memory. In addition, theinterconnection method may be any interconnection topology.

The machine learning operation device may have high compatibility andmay be coupled with various types of servers through the PCIE interface.

The present disclosure also discloses a combined processing device,which may include the above-mentioned machine learning operation device,universal interconnection interfaces, and other processing devices. Themachine learning operation device interacts with other processingdevices to perform user-specified operations. FIG. 6 is a schematicdiagram illustrated the combined processing device.

The other processing devices may include at least one of generalpurpose/dedicated processors such as a central processing unit (CPU), agraphics processing unit (GPU), a machine learning processor, and thelike. The number of processors included in other processing devices isnot limited. The other processing devices served as an interface betweenthe machine learning operation device and external data and control, mayinclude data handling, and perform the basic control of start and stopoperations of the machine learning operation device. The otherprocessing devices may also cooperate with the machine learningoperation device to complete the computing task.

The universal interconnection interfaces for sending data and controlinstructions between the machine learning operation device and the otherprocessing devices. The machine learning operation device may obtain theinput data required from the other processing devices, and writes theinput data required into on-chip storage devices of the machine learningoperation device. The machine learning operation device may acquirecontrol instructions from the other processing devices, and writes thecontrol instructions into on-chip control caches of the machine learningoperation device. The machine learning operation device may read data inthe storage module of the machine learning operation device and transmitthe data to the other processing devices.

In an example, a structure of another combined processing device is asillustrated in FIG. 7. A storage device may be further provided, thestorage device is respectively coupled with the machine learningoperation device and the other processing device. The storage device maybe configured to store data in the machine learning operation device andthe other processing devices, and is particularly suitable for storingdata to be processed which may not be completely stored in the internalstorage of the machine learning operation device or the other processingdevices.

The combined processing device may be used as an SOC on-chip system ofdevices such as mobile phones, robots, drones, video monitoring devices,etc., thereby effectively reducing the core area of control parts,increasing the processing speed, and reducing the overall powerconsumption. In this case, the universal interconnection interfaces ofthe combined processing device are coupled with certain components ofthe device. The components may include cameras, monitors, mice,keyboards, network cards, and WIFI interfaces.

In an example, a distributed system is also applied. The distributedsystem may include n1 main processors and n2 coprocessors. The n1 is aninteger greater than or equal to zero, and the n2 is an integer greaterthan or equal to one. The distributed system may be various types oftopologies including, but not limited to, topologies illustrated in FIG.3B, FIG. 3C, FIG. 11, and FIG. 12.

The main processor sends the input data, the decimal point position ofthe input data, and the computation instruction to the multiplecoprocessors. Alternatively, the main processor sends the input data,the decimal point position of the input data and the computationinstruction to some of the multiple coprocessors, and the coprocessorsfurther move the input data, the decimal point position of the inputdata, and the computation instruction to other coprocessors. Thecoprocessor may include the above-mentioned computation device, and thecomputation device performs operations on the input data according tothe above-identified method and the computation instruction to obtain anoperation result.

The input data may include, but not limited to, input neurons, weights,offset data, and the like.

The coprocessor sends the operation result directly to the mainprocessor. Alternatively, the coprocessor without coupling relationshipwith the main processor sends the operation result to the coprocessorcoupled with the main processor, and then the coprocessor coupled withthe main processor sends the operation result received to the mainprocessor.

In some examples, a chip may be provided. The chip may include theabove-mentioned machine learning operation device or the combinationprocessing device.

In some examples, a chip package structure may be provided. The chippackage structure may include the above-mentioned chip.

In some examples, a board is provided. The board may include theabove-mentioned chip package structure.

In some examples, an electronic device may be provided. The electronicdevice may include the above-mentioned board. A board is illustrated inFIG. 8. In addition to the chip 389, the board may also include othersupporting components including, but not limited to, a storage device390, a receiving device 391, and a control device 392.

The memory device 390 for storing data is coupled with the chip in thechip package structure via a bus. The memory device may include multiplesets of storage units 393. Each set of the storage units 393 is coupledwith the chip via the bus. It may be understood that each set of thestorage units 393 may be a double data rate synchronous dynamic randomaccess memory (DDR SDRAM).

The double data rate (DDR) is capable to double the speed of SDRAMwithout increasing the clock frequency. The DDR allows data to be readon rising and falling edges of the clock pulse. The DDR is twice as fastas the standard SDRAM. In one example, the storage device may includefour sets of the storage units. Each set of the memory cells may includemultiple DDR4 particles (chips). In one example, the chip may internallyinclude four 72-bit DDR4 controllers. 64 bits of the 72-bit DDR4controller are used for data transmission, and 8 bits of the 72-bit DDR4controller are used for error checking and correcting (ECC)verification. It should be understood that if DDR4-3200 particles areused in each set of the storage units, a theoretical bandwidth of datatransmission may reach 25600 MB/s.

In one example, each set of the memory cells may include multiple doublerate synchronous dynamic random access memories arranged in parallel.The DDR may transfer data twice in one clock cycle. A controller forcontrolling the DDR is provided in the chip for controlling datatransmission and data storage for each of the storage units.

The interface device is electrically coupled with the chip within thechip package structure. The interface device may be configured toimplement data transmission between the chip and external devices suchas a server and a computer. For example, in one example, the interfacedevice may be a standard PCIE interface. For example, the data to beprocessed is transmitted to the chip by the server through a standardPCIE interface to implement data transmission. Preferably, if the datato be processed is transmitted over the PCIE 3.0×16 interface, thetheoretical bandwidth may reach 16000 MB/s. In another example, theinterface device may also be another interface. The application does notlimit the specific expression of the other interfaces, and an interfaceunit capable of implementing the transfer function will be available. Inaddition, the computation result of the chip is still transmitted by theinterface device back to the external devices (such as a server).

The control device is electrically coupled with the chip. The controldevice may be configured to monitor the status of the chip.Specifically, the chip may be electrically coupled with the controldevice through an SPI interface. The control device may include a microcontroller unit (MCU). For example, the chip may include multipleprocessing chips, multiple processing cores, or multiple processingcircuits, and multiple loads may be driven. Therefore, the chip may bein different operating states such as multiple loads and light loads.The control device may implement the control of the operating states ofthe multiple processing chips, the multiple processing cores, and/or themultiple processing circuits in the chip.

The electronic device may include data processing devices, robots,computers, printers, smayners, tablets, smart terminals, mobile phones,driving recorders, navigators, sensors, cameras, servers, cloud servers,cameras, cameras, projectors, watches, headphones, mobile storage,wearable devices, vehicles, household appliances, and/or medicaldevices.

The vehicle may include an aircraft, a ship, and/or a car. The householdappliance may include a television, an air conditioner, a microwaveoven, a refrigerator, a rice cooker, a humidifier, a washing machine, anelectric lamp, a gas stove, a range hood. The medical device may includea nuclear magnetic resonance instrument, a B-ultrasound, and/or anelectrocardiograph.

FIG. 9 is a schematic flow chart of a method for executing machinelearning computations according to an example of the present disclosure.The method may include the following steps.

At S901, obtaining, by the computation device, first input data and acomputation instruction, where the first input data may include inputneurons and weights.

At S902, parsing, by the computation device, the computation instructionto obtain a data conversion instruction and an at least one operationinstruction,

where the data conversion instruction may include a data conversioninstruction including an opcode field and an opcode, where the opcodeindicates a function of the data conversion instruction, and the opcodefield of the data conversion instruction may include information of adecimal point position, a flag bit indicating a data type of the firstinput data, and an identifier of data type conversion.

At S903, converting, by the computation device, the first input datainto second input data according to the data conversion instruction,where the second input data is fixed-point data,

where the converting the first input data into second input data by thecomputation device according to the data conversion instructionincludes:

parsing the computation instruction to obtain information of the decimalpoint position, the flag bit indicating a data type of the first inputdata, and the data type conversion;

determining the data type of the first input data according to the flagbit indicating the data type of the first input data; and

converting the first input data into the second input data according tothe decimal point position and the data type conversion. The data typeof the first input data is inconsistent with that of the second inputdata.

If the first input data and the second input data are fixed-point data,the decimal point position of the first input data is inconsistent withthat of the second input data.

In an example, if the first input data is fixed-point data, the methodmay further include: deriving a decimal point position of at least oneintermediate result according to the decimal point position of the firstinput data, where the at least one intermediate result is derivedaccording to the first input data.

At S904, performing, by the computation device, operations on the secondinput data according to the multiple operation instructions to obtain acomputation result of the computation instruction.

The operation instruction may include a forward operation instructionand a reverse training instruction. In other words, in the process ofexecuting the forward operation instruction and the reverse traininginstruction (that is, the computation device performs the forwardoperation and/or the reverse training), the computation device mayconvert the data involved in operations into fixed-point data accordingto the example illustrated in FIG. 9 to perform fixed-point operations.

It should be noted that the detailed description of the foregoing stepsS901-S904 may be referred to the related description of the exampleillustrated in FIGS. 1 to 8, which are not described herein.

In a specific application scenario, the computation device may convertthe data involved in operations into fixed-point data, and adjust thedecimal point position of the fixed-point data. The specific process isillustrated in FIG. 10. The method may include the following steps.

At S1001, obtaining, by the computation device, the first input data.

The first input data is data involved in operations of m^(th) layer of amulti-layer network model, and the first input data is any type of data.For example, the first input data is fixed-point data, floating pointdata, integer data or discrete data, and m is an integer greater thanzero.

The m^(th) layer of the above-mentioned multilayer network model is alinear layer, and the linear layer may include, but not limited to, aconvolution layer and a fully coupled layer. The above-mentioned firstinput data may include input neurons, weights, output neurons, inputneuron derivatives, weight derivatives, and output neuron derivatives.

At S1002, determining, by the computation device, a decimal pointposition of the first input data and a bit width of the fixed-pointdata.

The bit width of fixed-point data of the first input data is bitsoccupied by the first input data represented by fixed-point data. Theabove-mentioned decimal point position is bits occupied by a fractionalpart of the first data represented by fixed-point data. The decimalpoint position may be configured to indicate the accuracy of thefixed-point data. Referring to the related description of FIG. 2A fordetails.

In an example, the first input data may be any type of data, and firstinput data a is converted into the second input data â according to thedecimal point position and the bit width of the fixed-point data, whichis described as follows.

$\hat{a} = \left\{ \begin{matrix}{{\left\lceil {a/2^{s}} \right\rceil*2^{s}},{{neg} \leq a \leq {pos}}} \\{{pos},{a > {pos}}} \\{{neg},{a < {neg}}}\end{matrix} \right.$

If the first input data a satisfies a condition of neg≤a≤pos, the secondinput data â is represented as |a/2^(s)|*2^(s). If the first input dataa is greater than pos, the second input data â is represented as pos. Ifthe first input data a is less than neg, the second input data â isrepresented as neg.

In an example, input neurons, weights, output neurons, input neuronderivatives, output neuron derivatives, and weight derivatives of aconvolutional layer and a fully coupled layer are represented byfixed-point data.

In an example, the bit width of fixed-point data of the input neuron maybe 8, 16, 32, 64, or other bits. Specifically, the bit width offixed-point data of the input neuron is 8.

In an example, the bit width of fixed-point data of the weight may be 8,16, 32, 64, or other bits. Specifically, the bit width of thefixed-point data of the weight is 8.

In an example, the bit width of fixed-point data of the input neuronderivative may be 8, 16, 32, 64, or other bits. Specifically, the bitwidth of fixed-point data of the input neuron derivative is 16 bits.

In an example, the bit width of fixed-point data of the output neuronderivative may be 8, 16, 32, 64, or other bits. Specifically, the bitwidth of fixed-point data of the output neuron derivative is 24 bits.

In an example, the bit width of fixed-point data of the weightderivative may be 8, 16, 32, 64, or other bits. Specifically, the bitwidth of fixed-point data of the weight derivative is 24 bits.

In an example, the data a with a large value in data involved in amulti-layer network model operation may adopt multiple representationmanners of fixed-point data. Referring to the related description ofFIG. 2B for details.

In an example, the first input data may be any type of data, and thefirst input data a is converted into the second input data â accordingto the decimal point position and the bit width of fixed-point data,which is described as follows.

$\hat{a} = \left\{ \begin{matrix}{{\sum\limits_{i}^{n}{\hat{a}}_{i}},{{neg} \leq a \leq {pos}}} \\{{pos},{a > {pos}}} \\{{neg},{a < {neg}}}\end{matrix} \right.$

If the first input data a satisfies a condition of neg≤a≤pos the secondinput data a is represented as

${{\hat{a}}_{i} = {\left\lceil \frac{a - {\hat{a}}_{i} - 1}{2^{si}} \right\rceil*2^{si}}},$

where â₀=0. If the first input data a is greater than pos the secondinput data â is represented as pos. If the first input data a is lessthan neg, the second input data â is represented as neg.

At S1003, initializing, by the computation device, a decimal pointposition of the first input data and adjusts the decimal point positionof the first input data.

The above decimal point position s need to be initialized anddynamically adjusted according to data of different categories, data ofdifferent neural network layers, and data of different iteration rounds.

An initialization process of the decimal point position s of the firstinput data is specifically described as follows, for example, thedecimal point position adopted by the fixed-point data is determined ifconverting the first input data at the first time.

Where the initializing the computation device the decimal pointpositions of the first input data includes:

initializing the decimal point positions of the first input dataaccording to the maximum absolute value of the first input data;initializing the decimal point position s of the first input dataaccording to the minimum absolute value of the first input data;initializing the decimal point positions of the first input dataaccording to relationship between different data types in the firstinput data; and initializing the decimal point positions of the firstinput data according to the empirical value constant.

Specifically, the above-mentioned initialization process is specificallydescribed as follows.

At step (a), the computation device initializes the decimal pointposition s of the first input data according to the maximum absolutevalue of the first input data.

The above-mentioned computation device specifically initializes thedecimal point positions of the first input data by performing anoperation shown by the following formula.

s_(a)=┌log₂a_(max)−bitnum+1┐

The a_(max) represents the maximum absolute value of the first inputdata. The bitnum represents the bit width of the fixed-point dataconverted from the first input data. The s_(a) represents the decimalpoint position of the first input data.

According to categories and network levels, the data involved inoperations may be divided into the input neuron X^((l)), the outputneuron Y^((l)), the weight W^((l)), the input neuron derivative ∇_(X)^((l)), the output neuron derivative ∇_(Y) ^((l)), and the weightderivative ∇_(W) ^((l)) of the l-th layer. The maximum absolute valuemay be searched by one of manners including searching by data category,searching by layer and data category, searching by layer, data category,and group. Determining the maximum absolute value of the first inputdata may include the following methods.

At step (a.1), the computation device searches the maximum absolutevalue by data category.

Specifically, the first input data may include each element a_(i) ^((l))in a vector/matrix, where the element a_(i) ^((l)) may be an inputneuron X^((l)), an output neuron Y^((l)), a weight W^((l)), an inputneuron derivative ∇_(X) ^((l)), an output neuron derivative ∇_(Y)^((l)), or a weight derivative ∇_(W) ^((l)). In other words, theabove-mentioned first input data may include input neurons, weights,output neurons, input neuron derivatives, weight derivatives, and outputneuron derivatives. The above-mentioned decimal point position of thefirst input data may include a decimal point position of the inputneuron, a decimal point position of the weight, a decimal point positionof the output neuron, a decimal point position of the input neuronderivative, a decimal point position of the weight derivative, and adecimal point position of the neuron derivative. The input neurons, theweights, the output neurons, the input neuron derivatives, the weightderivatives, and the output neuron derivatives are all represented in amatrix or vector form. The computation device acquires the maximumabsolute value (that is,

$a_{\max} = {\max\limits_{i,l}\left( {{abs}\left( a_{i}^{(l)} \right)} \right)}$

of each category data by traversing all elements in the vector/matrix ofeach layer of the above-mentioned multi-layer network model. The decimalpoint position s_(a) of the fixed-point data converted from the inputdata a of each category data is determined by a formula ofs_(a)=┌log₂a_(max)−bitnum+1┐.

At step (a.2), the computation device searches the maximum absolutevalue by layer and data category.

Specifically, the first input data may include each element a_(i) ^((l))in a vector/matrix, where the a^((l)) may be an input neuron X^((l)), anoutput neuron Y^((l)), a weight W^((l)), an input neuron derivative∇_(X) ^((l)), an output neuron derivative ∇_(Y) ^((l)), or a weightderivative ∇_(W) ^((l)). In other words, each layer of theabove-mentioned multi-layer network model may include input neurons,weights, output neurons, input neuron derivatives, weight derivatives,and output neuron derivatives. The above-mentioned decimal pointposition of the first input data may include a decimal point position ofthe input neuron, a decimal point position of the weight, a decimalpoint position of the output neuron, a decimal point position of theinput neuron derivative, a decimal point position of the weightderivative, and a decimal point position of the neuron derivative. Theinput neurons, the weights, the output neurons, the input neuronderivatives, the weight derivatives, and the output neuron derivativesare all represented in a matrix or vector form. The computation deviceacquires the maximum absolute value (that is,

$a_{\max}^{(l)} = {\max\limits_{l}\left( {{abs}\left( a_{i}^{(l)} \right)} \right)}$

of each category data by traversing all elements in the vector/matrix ofeach data of each layer of the above-mentioned multi-layer networkmodel. The decimal point position s_(a) ^((l)) of the input data a ofeach category of l-th layer is determined by a formula of s_(a)^((l))=┌log₂a_(max) ^((l))−bitnum+1┐.

At step (a.3), the computation device searches the maximum absolutevalue by layer, data category, and group.

Specifically, the first input data may include each element a_(i) ^((l))in a vector/matrix, where a^((l)) may be input neurons X^((l)), outputneurons Y^((l)), weights W^((l)), an input neuron derivative ∇_(X)^((l)), an output neuron derivative ∇_(Y) ^((l)), or a weight derivative∇_(W) ^((l)). In other words, data categories of each layer of theabove-mentioned multi-layer network model may include input neurons,weights, output neurons, input neuron derivatives, weight derivatives,and output neuron derivatives. The above-mentioned computation devicedivides data of each category of each layer of the above-mentionedmulti-layer network model into g groups, or into groups by otherarbitrary grouping rules. The computation device then traverses eachelement of each group of data in the g groups data corresponding to thedata of each category of each layer of the above-mentioned multi-layernetwork model, and obtains the element with the largest absolute value(that is,

$a_{\max}^{({g,l})} = {\max\limits_{l}\left( {{abs}\left( a_{i}^{({l,g})} \right)} \right)}$

in the group of data. The decimal point position a of data of each groupof the g groups data corresponding to each category of each layer isdetermined by a formula of s_(a) ^((l,g))=┌log₂(a_(max)^((l,g)))−bitnum+1┐.

The foregoing arbitrary grouping rules include, but are not limited to,rule for grouping according to data ranges, rule for grouping accordingto data training batches, and the like.

At step (b), the computation device initializes the decimal pointposition s of the first input data according to the maximum absolutevalue of the first input data.

Specifically, the computation device determines the minimum absolutevalue a_(min) of data to be quantized, and determines the fixed-pointprecision s by the following formula.

s_(a)=└log2(a_(min))┘

The above-mentioned a_(min) is the minimum absolute value of the firstinput data. For the process of acquiring a_(min), please refer to theabove-identified steps (a.1), (a.2), and (a.3).

At step (c), the computation device initializes the decimal pointposition s of the first input data according to the relationship betweendifferent data types in the first input data.

Specifically, the decimal point position s_(a) ^((l)) of the data typea^((l)) of any layer (such as the first layer) of the multi-layernetwork model is determined by the above-mentioned operation unit 12according to the decimal point position s_(b) ^((l)) of the data typeb^((l)) of the first layer and a formula of s_(a)^((l))=Σ_(b≈a)α_(b)s_(b) ^((l))+β_(b).

The a^((l)) and b^((l)) may be input neurons X^((l)), output neuronsY^((l)), weights W^((l)), input neuron derivatives ∇_(X) ^((l)), outputneuron derivatives ∇_(Y) ^((l)), or weight derivatives ∇_(W) ^((l)). Thea^((l)) and b^((l)) are integer constants.

At step (d), the computation device initializes the decimal pointposition s of the first input data according to the empirical valueconstant.

Specifically, the decimal point position s_(a) ^((l)) of the data typea^((l)) of any layer (such as the first layer) of the multi-layernetwork model may be artificially set as s_(a) ^((l))=c, where c is aninteger constant. The above-mentioned a^((l)) may be input neuronsX^((l)), output neurons Y^((l)), weights W^((l)), input neuronderivatives ∇_(X) ^((l)), output neuron derivatives ∇_(Y) ^((l)), orweight derivatives ∇_(W) ^((l)).

Further, an initialization value of the decimal point position of theinput neuron and an initialization value of the decimal point positionof the output neuron may be selected within the range of [−8, 8]. Aninitialization value of the decimal point position of the weight may beselected within the range of [−17, 8]. An initialization value of thedecimal point position of the input neuron derivative and aninitialization value of the decimal point position of the output neuronderivative may be selected within the range of [−40, −20]. Aninitialization value of the decimal point position of the weightderivative may be selected within the range of [−48, −12].

The method of dynamically adjusting the above-mentioned decimal pointposition of the data by the above-mentioned computation device will bespecifically described as follows.

The method of dynamically adjusting the above-mentioned decimal pointposition s by the above-mentioned computation device may includeadjusting the above-mentioned decimal point position s upwards (that is,value of the decimal point position s becomes larger) and adjusting theabove-mentioned decimal point position s downwards (that is, value ofthe decimal point position s becomes smaller). Specifically, theabove-mentioned decimal point position s is adjusted upwardly by asingle step according to the maximum absolute value of the first inputdata. The above-mentioned decimal point position s is adjusted upwardlystep by step according to the maximum absolute value of the first inputdata. The above-mentioned decimal point position s is adjusted upwardlyby a single step according to the first input data distribution. Theabove-mentioned decimal point position s is adjusted upwardly step bystep according to the first input data distribution. The above-mentioneddecimal point position s is adjusted downwardly according to theabsolute value of the first input date.

In case (a), the computation device upwardly adjusts the above-mentioneddecimal point position s by a single step according to the maximumabsolute value of the first input data.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). If themaximum absolute value a_(max) of the data in the first input data isgreater than and equal to pos, the decimal point position adjusted iss_new=┌log₂a_(max)−bitnum+1┐; otherwise, the above-mentioned decimalpoint position will not be adjusted, that is, s_new=s_old.

In case (b), the computation device upwardly adjusts the above-mentioneddecimal point position s step by step according to the maximum absolutevalue of the first input data.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). If themaximum absolute value a_(max) of the data in the first input data isgreater than and equal to pos, the decimal point position adjusted iss_new=s_old+1; otherwise, the above-mentioned decimal point positionwill not be adjusted, that is s_new=s_old.

In case (c), the computation device upwardly adjusts the above-mentioneddecimal point position s by single step according to the first inputdata distribution.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old).Statistical parameters of the absolute value of first input data areprocessed, such as a mean value a_(mean) of the absolute value and astandard deviation a_(std) of the absolute value. The maximum range ofdata is set as a_(max)=a_(mean)+na_(std). If a_(max)≥pos,s_new=┌log₂a_(max)−bitnum+1┐; otherwise, the above-mentioned mentioneddecimal point position will not be adjusted, that is s_new=s_old.

Further, the above-mentioned n may be two or three.

In case (d), the computation device upwardly adjusts the above-mentioneddecimal point position s step by step according to the first input datadistribution.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). Thestatistical parameters of the absolute value of first input data areprocessed, such as the mean value a_(mean) of the absolute value and thestandard deviation a_(std) of the absolute value. The maximum range ofdata is set as a_(max)=a_(mean)+na_(std), where n is three. Ifa_(max)≥pos, s_new=s_old+1; otherwise, the above-mentioned mentioneddecimal point position will not be adjusted, that is s_new=s_old.

In case (e), the computation device downwardly adjusts theabove-mentioned decimal point position s according to the maximumabsolute value of the first input date.

Assuming that the above-mentioned decimal point position is s_old beforebeing adjusted, the fixed-point data corresponding to the decimal pointposition s_old may represent data with a range of [neg, pos], wherepos=(2^(bitnum−1)−1)*2^(s_old), neg=−(2^(bitnum−1)−1)*2^(s_old). If themaximum absolute value a_(max) of the first input date is less than2^(s_old+(bitnum−n)) and s_old≥s_(min), s_new=s_old, where the n is aninteger constant, and the s_(min) may be an integer or a negativeinfinity.

Further, the above-mentioned n is three, and the above-mentioned s_(min)is −64.

In an example, an adjusting frequency of the decimal point position maybe determined as follows. The decimal point position of the first inputdata may never be adjusted. Alternatively, the decimal point position ofthe first input data may be adjusted every n first training cycles(i.e., iteration), where the n is a constant. Alternatively, the decimalpoint position of the first input data may be adjusted every n secondtraining cycles (i.e., epoch), where the n is a constant. Alternatively,the decimal point position of the first input data may be adjusted everyn first training periods or n second training periods, and adjusting thedecimal point position of the first input data every n first trainingperiods or second training periods, and then the n may be adjusted, thatis, n=αn, α is greater than one. Alternatively, the decimal pointposition of the first input data may be adjusted every n first trainingperiod or second training period, and the n is gradually decreased asthe number of training rounds increases.

Further, the decimal point position of the input neuron, the decimalpoint position of the weight, and the decimal point position of theoutput neuron may be adjusted every 100 first training cycles. Thedecimal point position of the input neuron derivative and the decimalpoint position of the output neuron derivative may be adjusted every 20first training cycles.

It should be noted that the first training period is the time requiredto train a batch of samples, and the second training period is the timerequired to perform training for all training samples.

It should be noted that the decimal point position of the above data isinitialized and adjusted according to the average value or theintermediate value of the absolute value of the above data. Specificdetails may refer to the above-identified related description ofinitializing and adjusting the decimal point position of the above dataaccording to the maximum value of the absolute value of the data, whichwill not be described herein.

It is to be noted that, for the sake of simplicity, the foregoing methodexamples are described as a series of action combinations, however, itwill be appreciated by those skilled in the art that the presentdisclosure is not limited by the sequence of actions described.According to the present disclosure, certain steps or operations may beperformed in other order or simultaneously. Besides, it will beappreciated by those skilled in the art that the examples described inthe specification are exemplary examples and the actions and modulesinvolved are not necessarily essential to the present disclosure.

In the foregoing examples, the description of each example has its ownemphasis. For the parts not described in detail in one example,reference may be made to related descriptions in other examples.

In the examples of the disclosure, it should be understood that, theapparatus disclosed in examples provided herein may be implemented inother manners. For example, the device/apparatus examples describedabove are merely illustrative; for instance, the division of the unit isonly a logical function division and there may be other manners ofdivision during actual implementations, for example, multiple units orcomponents may be combined or may be integrated into another system, orsome features may be ignored, omitted, or not performed. In addition,coupling or communication connection between each illustrated ordiscussed component may be direct coupling or communication connection,or may be indirect coupling or communication among devices or units viasome interfaces, and may be electrical connection, mechanicalconnection, or other forms of connection.

The units described as separate components may or may not be physicallyseparated, the components illustrated as units may or may not bephysical units, that is, they may be in the same place or may bedistributed to multiple network elements. All or part of the units maybe selected according to actual needs to achieve the purpose of thetechnical solutions of the examples.

In addition, the functional units in various examples of the presentdisclosure may be integrated into one processing unit, or each unit maybe physically present, or two or more units may be integrated into oneunit. The above-mentioned integrated unit may be implemented in the formof hardware or a software function unit.

The integrated unit may be stored in a computer-readable memory if it isimplemented in the form of a software functional unit and is sold orused as a separate product. Based on such understanding, the technicalsolutions of the present disclosure essentially, or the part of thetechnical solutions that contributes to the related art, or all or partof the technical solutions, may be embodied in the form of a softwareproduct which is stored in a memory and may include instructions forcausing a computer device (which may be a personal computer, a server,or a network device and so on) to perform all or part of the stepsdescribed in the various examples of the present disclosure. The memorymay include various medium capable of storing program codes, such as aUSB (universal serial bus) flash disk, a read-only memory (ROM), arandom access memory (RAM), a removable hard disk, Disk, compact disc(CD), or the like.

It will be understood by those of ordinary skill in the art that all ora part of the various methods of the examples described above may beaccomplished by means of a program to instruct associated hardware. Theprogram may be stored in a computer-readable memory, which may include aflash memory, a read-only memory (ROM), a random-access memory (RAM), adisk or a compact disc (CD), and so on.

The examples of the present disclosure are described in detail above,specific examples are used herein to describe the principle andimplementation manners of the present disclosure. The description of theabove examples is merely used to help understand the method and the coreidea of the present disclosure. Meanwhile, those skilled in the art maymake modifications to the specific implementation manners and theapplication scope according to the idea of the present disclosure. Insummary, the contents of the specification should not be construed aslimiting the present disclosure.

What is claimed is:
 1. A computation device, comprising: a storage unit,a controller unit, and a conversion unit, wherein: the storage unit isconfigured to store a first input data, information of a decimal pointposition, a flag bit indicating a data type of the first input data, andan identifier of data type conversion; the controller unit is configuredto obtain a plurality of operation instructions, the first input data,the information of the decimal point position, the flag bit indicatingthe data type of the first input data, and the identifier of data typeconversion; and transmit the first input data, information of thedecimal point position, the flag bit indicating the data type of thefirst input data, and the identifier of data type conversion to theconversion unit; and the conversion unit is configured to convert thefirst input data into a second input data according to the flag bitindicating the data type of the first input data, and the identifier ofdata type conversion, the second input data being fixed-point data. 2.The computing device of claim 1, wherein the computing device isconfigured to perform a machine learning computation, and furtherincludes an operation unit, wherein: the storage unit is furtherconfigured to store operation instructions, the controller unit isfurther configured to obtain the plurality of operation instructionsfrom the storage unit, and transmit the plurality of operationinstructions to the operation unit, the conversion unit is furtherconfigured to transmit the second input data to the operation unit, andthe operation unit is configured to operate the second input dataaccording to the plurality of operation instructions to obtain acomputation result.
 3. The computation device of claim 2, wherein: themachine learning computation includes an artificial neural networkoperation, the first input data includes an input neuron and a weight,and the computation result is an output neuron.
 4. The computationdevice of claim 2, wherein the operation unit includes a primaryprocessing circuit and a plurality of secondary processing circuits,wherein: the primary processing circuit is configured to performpre-processing on the second input data, and transmit data between theplurality of secondary processing circuits and the primary processingcircuit, the plurality of secondary processing circuits is configured toperform intermediate operations to obtain a plurality of intermediateresults according to the second input data, and the plurality ofoperation instructions transmitted from the primary processing circuit,and transmit the plurality of intermediate results to the primaryprocessing circuit, and the primary processing circuit is furtherconfigured to perform post-processing on the plurality of intermediateresults to obtain the computation result of the computation instruction.5. The computation device of claim 4, further comprising a direct memoryaccess (DMA) unit, and the storage unit further includes a register anda cache, wherein: the cache includes a scratch pad cache and isconfigured to store the first input data, the register is configured tostore scalar data in the first input data, information of a decimalpoint position, a flag bit indicating a data type of the first inputdata, and an identifier of data type conversion in the first input data,and the DMA unit is configured to read or store data from the storagedata.
 6. The computation device of claim 2, wherein when the first inputdata is fixed-point data, the operation unit further includes: aderivation unit configured to derive a decimal point position of one ormore intermediate results according to the decimal point position of thefirst input data, wherein the one or more intermediate results areobtained according to the first input data.
 7. The computation device ofclaim 6, wherein the operation unit further includes: a data cache unitconfigured to cache one or more intermediate results.
 8. The computationdevice of claim 3, wherein the operation unit includes a tree module,wherein the tree module includes a root port coupled with the primaryprocessing circuit and a plurality of branch ports coupled with theplurality of secondary processing circuits, wherein the tree module isconfigured to forward data, and operation instructions transmitted amongthe primary processing circuit and the plurality of secondary processingcircuits; and wherein the tree module is an n-tree structure, the nbeing an integer greater than or equal to two.
 9. The computation deviceof claim 3, wherein the operation unit further includes a branchprocessing circuit, wherein: the primary processing circuit isspecifically configured to: determine that the input neurons arebroadcast data, and the weights are distribution data, divide thedistribution data into a plurality of data blocks, and transmit at leastone of the plurality of data blocks, the broadcast data, and at leastone of the plurality of operation instructions to the branch processingcircuit, the branch processing circuit is configured to forward the datablocks, the broadcast data, and operation instructions transmitted amongthe primary processing circuit and the plurality of secondary processingcircuits, the plurality of secondary processing circuits is configuredto perform operations on the data blocks received and the broadcast datareceived according to the plurality of operation instructions to obtaina plurality of intermediate results, and to transmit the plurality ofintermediate results to the branch processing circuit, and the primaryprocessing circuit is further configured to perform post-processing onthe plurality of intermediate results received from the branchprocessing circuits to obtain a computation result of the computationinstruction, and to send the computation result of the computationinstruction to the controller unit.
 10. The computation device of claim3, wherein the plurality of secondary processing circuits is distributedin an array, wherein: each secondary processing circuit is coupled withadjacent other secondary processing circuits, and the primary processingcircuit is coupled with K secondary processing circuits of the pluralityof secondary processing circuits, the K secondary processing circuitscomprise n secondary processing circuits in the first row, n secondaryprocessing circuits in the mth row, and m secondary processing circuitsin the first column, the K secondary processing circuits are configuredto forward data and instructions transmitted among the primaryprocessing circuit and the plurality of secondary processing circuits,the primary processing circuit is further configured to: determine thatthe input neurons are broadcast data, the weights are distribution data,divide the distribution data into a plurality of data blocks, andtransmit at least one of the plurality of data blocks and at least oneof the plurality of operation instructions to the K secondary processingcircuits, wherein the K secondary processing circuits are configured toconvert the data transmitted among the primary processing circuit andthe plurality of secondary processing circuits, the plurality ofsecondary processing circuits is configured to perform operations on thedata blocks received according to the plurality of operationinstructions to obtain a plurality of intermediate results, and totransmit the plurality of intermediate results to the K secondaryprocessing circuits, and the primary processing circuit is configured toprocess the plurality of intermediate results received from the Ksecondary processing circuits to obtain the computation result of thecomputation instruction, and send the computation result of thecomputation instruction to the controller unit.
 11. The computationdevice of claim 8, wherein: the primary processing circuit isspecifically configured to perform a combined ranking processing on theplurality of intermediate results received from the plurality ofprocessing circuits to obtain the computation result of the computationinstruction, or the primary processing circuit is configured to acombined ranking processing and an activation processing on theplurality of intermediate results received from the plurality ofprocessing circuits to obtain the computation result of the computationinstruction, wherein the primary processing circuit includes one or anycombination of an activation processing circuit and an additionprocessing circuit, wherein: the activation processing circuit isconfigured to perform an activation operation on data in the primaryprocessing circuit, and the addition processing circuit is configured toperform an addition operation or an accumulation operation, and theplurality of secondary processing circuit includes multiplicationprocessing circuits configured to perform multiplication operations onthe data blocks received to obtain product results.
 12. A machinelearning operation device, comprising the computation device of claim 2,wherein the one or more computation devices are configured to obtaindata to be operated and control information from other processingdevices, to perform a specified machine learning computation, and totransmit an execution result to the other processing devices through I/Ointerfaces, wherein: when the machine learning operation device includesa plurality of the computation devices, the plurality of computationdevices is configured to couple and transmit data with each otherthrough a specific structure, and the plurality of computation devicesis configured to interconnect and to transmit data through a fastexternal device interconnection PCIE (peripheral component interfaceexpress) bus to support larger-scale machine learning computations,share the same one control system or have respective control systems,share the same one memory or have respective memories, and deploy aninterconnection manner of any arbitrary interconnection topology.
 13. Acombined processing device, comprising the machine learning operationdevice of claim 12, universal interconnection interfaces, otherprocessing devices and a storage device, wherein: the machine learningoperation device is configured to interact with the other processingdevices to jointly perform user-specified computing operations, and thestorage device is configured to couple with the machine learningoperation device and the other processing devices respectively forstoring data of the machine learning operation device and the otherprocessing devices.
 14. A neural network chip, comprising the machinelearning operation device of claim
 12. 15. An electronic device,comprising the neural network chip of claim
 14. 16. A board, comprisinga storage device, an interface device, a control device, and the neuralnetwork chip of claim 15, wherein the neural network chip isrespectively coupled with the storage device, the control device, andthe interface device, the storage device is configured to store data,the interface device is configured to implement data transmissionbetween the neural network chip and external devices, and the controldevice is configured to monitor a status of the neural network chip,wherein the storage device includes a plurality of groups of storageunits, wherein each group of the plurality of groups of the storageunits is coupled with the neural network chip through a bus, and thestorage unit is a double data rate (DDR) synchronous dynamic randomaccess memory (SDRAM), wherein the neural network chip includes a DDRcontroller for controlling data transmission and data storage of each ofthe storage units, and wherein the interface device includes standardPCIE interfaces.
 17. A computation method, comprising: obtaining, by thecontroller unit, the first input data, information of a decimal pointposition, a flag bit indicating a data type of the first input data, andan identifier of data type conversion, and converting, by the conversionunit, the first input data into second input data according to the flagbit indicating the data type of the first input data, and the identifierof data type conversion, wherein the second input data is fixed-pointdata.
 18. The computation method of claim 17, wherein the computationmethod is configured to perform a machine learning computation, andcomputation the method further includes: obtaining, by the operationunit, a plurality of operation instructions from the storage unit, andoperating the second input data according to the plurality of operationinstructions to obtain a computation result.
 19. The computation methodof claim 18, wherein: the machine learning computation includes theartificial neural network operation, the first input data includes aninput neuron and a weight, and the computation result is an outputneuron.
 20. The computation method of claim 17, wherein: when the firstinput data, and the second input data are both fixed-point data, thedecimal point position of the first input data is inconsistent with thatof the second input data.
 21. The computation method of claim 20,wherein when the first input data is fixed-point data, the methodfurther includes: deriving a decimal point position of one or moreintermediate results according to the decimal point position of thefirst input data, wherein the one or more intermediate results areobtained by operating according to the first input data.